OSM report 2022-2023
Marissa Dyck
2024-05-08
The first two chunks of this r markdown file after the r setup allow for plot zooming, but it also means that the html file must be opened in a browser to view the document properly. When it knits in RStudio the preview will appear empty but the html when opened in a browser will have all the info and you can click on each plot to Zoom in on it.
Before you begin
Notes
A few notes about this script.
If you are running this with the 2022-2023 data make sure you download the whole (OSM_2022-2023 GitHub repository)[https://github.com/ACMElabUvic/OSM_2022-2023] from the ACMElabUvic GitHub. This will ensure you have all the files, data, and proper folder structure you will need to run this code and associated analyses.
Also make sure you open RStudio through the R project (OSM_2022-2023.Rproj) this will automatically set your working directory to the correct place (wherever you saved the repository) and ensure you don’t have to change the file paths for some of the data.
Lastly, if you are looking to adapt this code for a future year of data, you will want to ensure you have run the 2_ACME_landscape_covariate_exploration_script.Rmd with your data as there is much data formatting, cleaning, and restructuring that has to be done before this code will work. Helpful note: The files are numbered in the order they are used for this analysis.
If you have question please email the most recent author, currently
Marissa A. Dyck
Postdoctoral research fellow
University of Victoria
School of Environmental Studies
Email: marissadyck17@gmail.com
(update/add authors as needed)
Install packages
If you don’t already have the following packages installed, use the code below to install them.
install.packages('tidyverse')
install.packages('ggpubr')
install.packages('corrplot')
install.packages('Hmisc')
install.packages('glmmTMB')
install.packages('MuMIn')
install.packages('TMB', type = 'source')
install.packages('rphylopic')
install.packages('broom')Load libraries
Then load the packages to your library.
library(tidyverse) # data tidying, visualization, and much more; this will load all tidyverse packages, can see complete list using tidyverse_packages()
library(ggpubr) # make modificaions to plot for publication (arrange plots)
library(PerformanceAnalytics) #Used to generate a correlation plot
library(Hmisc) # used to generate histograms for all variables in data frame
library(glmmTMB) #Constructing GLMMs## Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
## TMB was built with Matrix version 1.4.1
## Current Matrix version is 1.5.3
## Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' packagelibrary(MuMIn) # for model selection
library(rphylopic) # add animal silhouettes to graphs
library(broom) # extracting odds ratios in a tidy formatSummary statistics
Load detection data
Read in saved and cleaned detection data for the 6 LUs from 2021-2022 and 2022-2023 e.g., the 1_ACME_camera_script_9-2-2024.R.
These are two separate files from the two different fiscal years, so they need to be imported and then merged into one data file for plotting. Since both are stored in the data/processed/ folder we can read them both in as a list with purrr.
# detection data
# read in saved and cleaned detection data from the ACME_camera_script_9-2-2024.R
detections <- file.path('data/processed',
c('OSM_ind_det_2021.csv',
'OSM_ind_det_2022.csv')) %>%
map(~.x %>%
read_csv(.) %>%
# ensure the array, site, species, and event_id read in as factors
mutate_if(is.character,
as.factor)) %>%
# give names to each data frame in list
purrr::set_names('dets_2021',
'dets_2022') # R doesn't like when they are just numbers, you can make it work but it's annoying to call the data frame later so I've called them dets_year## Rows: 6696 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): array, site, species, event_id
## dbl (3): month, year, timediff
## dttm (1): datetime
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 14063 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): array, site, species, event_id
## dbl (3): month, year, timediff
## dttm (1): datetime
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(detections)## List of 2
## $ dets_2021: tibble [6,696 × 8] (S3: tbl_df/tbl/data.frame)
## ..$ array : Factor w/ 2 levels "LU2","LU3": 1 1 1 1 1 1 1 1 1 1 ...
## ..$ site : Factor w/ 78 levels "LU2_03","LU2_05",..: 1 1 1 1 1 1 1 1 1 1 ...
## ..$ species : Factor w/ 35 levels "Arctic hare",..: 28 28 35 35 35 35 35 35 35 35 ...
## ..$ datetime: POSIXct[1:6696], format: "2021-07-15 11:40:07" "2022-02-06 15:11:06" ...
## ..$ month : num [1:6696] 7 2 7 8 8 8 8 8 8 9 ...
## ..$ year : num [1:6696] 2021 2022 2021 2021 2021 ...
## ..$ timediff: num [1:6696] NA 296839 NA 28519 13230 ...
## ..$ event_id: Factor w/ 6696 levels "E1000000","E1000001",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ dets_2022: tibble [14,063 × 8] (S3: tbl_df/tbl/data.frame)
## ..$ array : Factor w/ 4 levels "LU01","LU13",..: 1 1 1 1 1 1 1 1 1 1 ...
## ..$ site : Factor w/ 155 levels "LU01_06","LU01_10",..: 1 1 1 1 1 1 1 1 1 1 ...
## ..$ species : Factor w/ 39 levels "ATVer","Beaver",..: 31 31 38 38 38 38 38 38 38 38 ...
## ..$ datetime: POSIXct[1:14063], format: "2022-06-17 10:01:52" "2023-09-10 12:51:15" ...
## ..$ month : num [1:14063] 6 9 6 7 7 7 8 8 8 8 ...
## ..$ year : num [1:14063] 2022 2023 2022 2022 2022 ...
## ..$ timediff: num [1:14063] NA 648166 NA 31847 21429 ...
## ..$ event_id: Factor w/ 14063 levels "E0","E1","E10",..: 1 2 5176 6287 7398 8509 9620 10731 11842 12953 ...Merge data
Now we need to merge these two files to make plotting easier.
They have the same columns so we could just the base R
rbind() function, but in case there are differences in the
columns in the future, let’s use the cleaner
dplyr::bind_rows() function
# Join two years of detection data
detections_merged <- dplyr::bind_rows(detections$dets_2021,
detections$dets_2022)
# check new data
str(detections_merged)## tibble [20,759 × 8] (S3: tbl_df/tbl/data.frame)
## $ array : Factor w/ 6 levels "LU2","LU3","LU01",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ site : Factor w/ 233 levels "LU2_03","LU2_05",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ species : Factor w/ 42 levels "Arctic hare",..: 28 28 35 35 35 35 35 35 35 35 ...
## $ datetime: POSIXct[1:20759], format: "2021-07-15 11:40:07" "2022-02-06 15:11:06" ...
## $ month : num [1:20759] 7 2 7 8 8 8 8 8 8 9 ...
## $ year : num [1:20759] 2021 2022 2021 2021 2021 ...
## $ timediff: num [1:20759] NA 296839 NA 28519 13230 ...
## $ event_id: Factor w/ 20759 levels "E1000000","E1000001",..: 1 2 3 4 5 6 7 8 9 10 ...Data checks
Let’s check to make sure the data looks good after merging
Arrays and sites
Let’s check that there are the correct number of levels for array and sites, there should be 6 arrays and 233 sites (155 from 2022-2023 and 78 from 2021-2022)
str(detections_merged)## tibble [20,759 × 8] (S3: tbl_df/tbl/data.frame)
## $ array : Factor w/ 6 levels "LU2","LU3","LU01",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ site : Factor w/ 233 levels "LU2_03","LU2_05",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ species : Factor w/ 42 levels "Arctic hare",..: 28 28 35 35 35 35 35 35 35 35 ...
## $ datetime: POSIXct[1:20759], format: "2021-07-15 11:40:07" "2022-02-06 15:11:06" ...
## $ month : num [1:20759] 7 2 7 8 8 8 8 8 8 9 ...
## $ year : num [1:20759] 2021 2022 2021 2021 2021 ...
## $ timediff: num [1:20759] NA 296839 NA 28519 13230 ...
## $ event_id: Factor w/ 20759 levels "E1000000","E1000001",..: 1 2 3 4 5 6 7 8 9 10 ...# check the levels for array and sites
levels(detections_merged$array)## [1] "LU2" "LU3" "LU01" "LU13" "LU15" "LU21"levels(detections_merged$site)## [1] "LU2_03" "LU2_05" "LU2_100" "LU2_101" "LU2_106" "LU2_110"
## [7] "LU2_119" "LU2_123" "LU2_13" "LU2_137" "LU2_143" "LU2_15"
## [13] "LU2_153" "LU2_155" "LU2_17" "LU2_18" "LU2_20" "LU2_21"
## [19] "LU2_24" "LU2_25" "LU2_26" "LU2_27" "LU2_29" "LU2_30"
## [25] "LU2_31" "LU2_32" "LU2_33" "LU2_34" "LU2_36" "LU2_38"
## [31] "LU2_39" "LU2_40" "LU2_42" "LU2_44" "LU2_46" "LU2_47"
## [37] "LU2_50" "LU2_51" "LU2_52" "LU2_54" "LU2_56" "LU2_57"
## [43] "LU3_05" "LU3_07" "LU3_10" "LU3_102" "LU3_103" "LU3_111"
## [49] "LU3_126" "LU3_129" "LU3_13" "LU3_131" "LU3_137" "LU3_14"
## [55] "LU3_147" "LU3_15" "LU3_17" "LU3_18" "LU3_19" "LU3_20"
## [61] "LU3_21" "LU3_25" "LU3_27" "LU3_28" "LU3_32" "LU3_36"
## [67] "LU3_38" "LU3_39" "LU3_40" "LU3_41" "LU3_44" "LU3_45"
## [73] "LU3_46" "LU3_48" "LU3_49" "LU3_50" "LU3_51" "LU3_52"
## [79] "LU01_06" "LU01_10" "LU01_11" "LU01_13" "LU01_22" "LU01_25"
## [85] "LU01_27" "LU01_30" "LU01_32" "LU01_36" "LU01_40" "LU01_41"
## [91] "LU01_43" "LU01_44" "LU01_45" "LU01_46" "LU01_47" "LU01_48"
## [97] "LU01_60" "LU01_63" "LU01_64" "LU01_66" "LU01_67" "LU01_70"
## [103] "LU01_71" "LU01_72" "LU01_73" "LU01_74" "LU01_75" "LU01_76"
## [109] "LU01_77" "LU01_78" "LU01_79" "LU01_80" "LU01_82" "LU01_83"
## [115] "LU01_84" "LU01_85" "LU01_86" "LU13_03" "LU13_05" "LU13_06"
## [121] "LU13_08" "LU13_11" "LU13_12" "LU13_128" "LU13_13" "LU13_131"
## [127] "LU13_14" "LU13_15" "LU13_16" "LU13_17" "LU13_18" "LU13_19"
## [133] "LU13_20" "LU13_21" "LU13_22" "LU13_26" "LU13_27" "LU13_30"
## [139] "LU13_32" "LU13_33" "LU13_34" "LU13_35" "LU13_36" "LU13_37"
## [145] "LU13_38" "LU13_41" "LU13_43" "LU13_45" "LU13_47" "LU13_49"
## [151] "LU13_51" "LU13_52" "LU13_53" "LU13_55" "LU13_56" "LU13_57"
## [157] "LU13_59" "LU13_70" "LU15_01" "LU15_02" "LU15_03" "LU15_04"
## [163] "LU15_07" "LU15_08" "LU15_09" "LU15_10" "LU15_11" "LU15_12"
## [169] "LU15_14" "LU15_15" "LU15_16" "LU15_17" "LU15_18" "LU15_19"
## [175] "LU15_20" "LU15_21" "LU15_22" "LU15_23" "LU15_24" "LU15_25"
## [181] "LU15_26" "LU15_27" "LU15_28" "LU15_29" "LU15_30" "LU15_31"
## [187] "LU15_32" "LU15_34" "LU15_36" "LU15_37" "LU15_40" "LU15_41"
## [193] "LU15_43" "LU15_44" "LU15_46" "LU15_58" "LU15_61" "LU21_06"
## [199] "LU21_09" "LU21_10" "LU21_100" "LU21_105" "LU21_106" "LU21_107"
## [205] "LU21_109" "LU21_114" "LU21_116" "LU21_119" "LU21_122" "LU21_126"
## [211] "LU21_14" "LU21_153" "LU21_16" "LU21_164" "LU21_21" "LU21_23"
## [217] "LU21_27" "LU21_32" "LU21_36" "LU21_41" "LU21_52" "LU21_56"
## [223] "LU21_57" "LU21_59" "LU21_63" "LU21_68" "LU21_74" "LU21_78"
## [229] "LU21_82" "LU21_871" "LU21_93" "LU21_97" "LU21_98"Everything looks good
NAs
Let’s ensure no NAs were introduced where there shouldn’t be during the joining process
# check for NAs introduced during data merge
summary(detections_merged)## array site species
## LU2 :4711 LU01_47: 405 White-tailed deer:6141
## LU3 :1985 LU01_84: 396 Snowshoe hare :4571
## LU01:6283 LU01_66: 383 Red squirrel :2201
## LU13:1972 LU01_27: 320 Black bear :1997
## LU15:2951 LU2_39 : 310 Coyote :1285
## LU21:2857 LU2_50 : 292 Moose : 693
## (Other):18653 (Other) :3871
## datetime month year
## Min. :2021-07-08 10:21:40.00 Min. : 1.000 Min. :2021
## 1st Qu.:2022-04-25 01:14:32.50 1st Qu.: 5.000 1st Qu.:2022
## Median :2022-11-03 19:10:30.00 Median : 8.000 Median :2022
## Mean :2022-10-09 18:09:20.27 Mean : 7.204 Mean :2022
## 3rd Qu.:2023-05-05 01:40:45.50 3rd Qu.: 9.000 3rd Qu.:2023
## Max. :2023-10-02 11:08:57.00 Max. :12.000 Max. :2023
##
## timediff event_id
## Min. : 30 E1000000: 1
## 1st Qu.: 1427 E1000001: 1
## Median : 5270 E1000002: 1
## Mean : 30292 E1000003: 1
## 3rd Qu.: 18443 E1000004: 1
## Max. :653883 E1000005: 1
## NA's :2446 (Other) :20753THe only NAs are in the timediff column which is what we expect since any of the first observations won’t have a timediff.
Data Formatting
In order to get plots that have the same formatting as last years’ report we have to do a bit of data formatting. First we need to make sure we are including the same relevant species (some were ignored for last years’ report or grouped together)
Last years report had the following species
- white-tailed deer
- snowshoe hare
- black bear
- coyote
- red squirrel
- fisher
- unknown
- moose
- lynx
- spruce grouse
- red fox
- striped skunk
- ruffed grouse
- owl
- grey wolf
- domestic dog
- cougar
- raven
- other
- mule deer
And they grouped all humans except for staff as ‘Humans’. Let’s look at the species we have in the combined years of data and try to format it the same way
detections_merged %>%
# group by array and species
group_by(species) %>%
summarise(n = n()) %>%
# have R print everything
print(n = nrow(.))## # A tibble: 42 × 2
## species n
## <fct> <int>
## 1 Arctic hare 1
## 2 ATVer 41
## 3 Beaver 4
## 4 Black bear 1997
## 5 Caribou 115
## 6 Cougar 37
## 7 Coyote 1285
## 8 Domestic dog 15
## 9 Elk 1
## 10 Fisher 257
## 11 Grey jay 66
## 12 Grey wolf 224
## 13 Human 16
## 14 Lynx 525
## 15 Marten 220
## 16 Moose 693
## 17 Mule deer 2
## 18 Other 11
## 19 Other birds 219
## 20 Owl 15
## 21 Raven 10
## 22 Red fox 127
## 23 Red squirrel 2201
## 24 Ruffed grouse 90
## 25 Snowmobiler 16
## 26 Snowshoe hare 4571
## 27 Spruce grouse 93
## 28 Staff 461
## 29 Striped skunk 65
## 30 Unknown 663
## 31 Unknown canid 76
## 32 Unknown deer 340
## 33 Unknown mustelid 66
## 34 Unknown ungulate 34
## 35 White-tailed deer 6141
## 36 Canada goose 4
## 37 Hunter 1
## 38 Long-tailed weasel 17
## 39 Otter 7
## 40 Porcupine 5
## 41 Short-tailed weasel 19
## 42 Wolverine 8Hmmm there is one instance of an arctic hare, check that this isn’t meant to be a snowshoe hare and fix later if needed.
Now let’s create a new data frame (tibble) to work with for the OSM figure summaries specifically
I personally would lump all the unknown together and all the birds together but for the sake of consistency with last year’s figures we will remove the same entries and keep the birds separate, let’s create a vector of entries to drop
species_drop <- c('Staff',
'Unknown deer',
'Unknown ungulate',
'Unknown canid',
'Unknown mustelid',
'Other birds',
'Arctic hare')
# now we can create the new data frame with some changes consistent w/ choices made for 2021-2022
detections_merged <- detections_merged %>%
# for summarizing, lets lump all the recreational humans into "Humans"
mutate(species = recode_factor(species,
"Snowmobiler" = "Human",
"ATVer" = "Human",
'Hunter' = 'Human')) %>%
# remove species we don't want to plot
filter(!species %in% species_drop)
# look at data
str(detections_merged)## tibble [19,562 × 8] (S3: tbl_df/tbl/data.frame)
## $ array : Factor w/ 6 levels "LU2","LU3","LU01",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ site : Factor w/ 233 levels "LU2_03","LU2_05",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ species : Factor w/ 39 levels "Human","Arctic hare",..: 33 33 33 33 33 33 33 33 33 33 ...
## $ datetime: POSIXct[1:19562], format: "2021-07-18 08:59:30" "2021-08-07 04:21:14" ...
## $ month : num [1:19562] 7 8 8 8 8 8 8 9 9 9 ...
## $ year : num [1:19562] 2021 2021 2021 2021 2021 ...
## $ timediff: num [1:19562] NA 28519 13230 4290 7337 ...
## $ event_id: Factor w/ 20759 levels "E1000000","E1000001",..: 3 4 5 6 7 8 9 10 11 12 ...Subset data
We will also want to subset the data by landscape unit (LU) and generate a new data frame for each LU to use for plotting
I’m not great at writing loops, so let’s see how this shit goes… probably bad but who knows
array_frames <- list()
for (i in unique(detections_merged$array)){
#Subset data based on radius
df <- detections_merged %>%
filter(array == i)
# list of dataframes
array_frames <- c(array_frames, list(df))
}
# inspect one data frame
print(array_frames[[1]]) # this is for LU2## # A tibble: 4,592 × 8
## array site species datetime month year timediff event_id
## <fct> <fct> <fct> <dttm> <dbl> <dbl> <dbl> <fct>
## 1 LU2 LU2_03 White-tailed … 2021-07-18 08:59:30 7 2021 NA E1000002
## 2 LU2 LU2_03 White-tailed … 2021-08-07 04:21:14 8 2021 28519. E1000003
## 3 LU2 LU2_03 White-tailed … 2021-08-16 08:51:21 8 2021 13230. E1000004
## 4 LU2 LU2_03 White-tailed … 2021-08-19 08:21:29 8 2021 4290. E1000005
## 5 LU2 LU2_03 White-tailed … 2021-08-24 10:39:23 8 2021 7337. E1000006
## 6 LU2 LU2_03 White-tailed … 2021-08-26 09:17:02 8 2021 2797. E1000007
## 7 LU2 LU2_03 White-tailed … 2021-08-31 19:13:33 8 2021 7796 E1000008
## 8 LU2 LU2_03 White-tailed … 2021-09-10 05:03:31 9 2021 13550. E1000009
## 9 LU2 LU2_03 White-tailed … 2021-09-16 17:10:41 9 2021 9363. E1000010
## 10 LU2 LU2_03 White-tailed … 2021-09-16 19:19:24 9 2021 127 E1000011
## # ℹ 4,582 more rows… I think this worked
Now let’s change names of list items using purrr, couldn’t figure out how to name them in the loop, you don’t necessarily need to do this because we change the names in the next section, but I like having things named
array_frames <- array_frames %>%
purrr::set_names('LU02',
'LU03',
'LU01',
'LU13',
'LU15',
'LU21')
# inspect each data frame
head(array_frames$LU01)## # A tibble: 6 × 8
## array site species datetime month year timediff event_id
## <fct> <fct> <fct> <dttm> <dbl> <dbl> <dbl> <fct>
## 1 LU01 LU01_06 White-tailed … 2022-06-18 11:09:19 6 2022 NA E2
## 2 LU01 LU01_06 White-tailed … 2022-07-10 13:56:10 7 2022 31847. E3
## 3 LU01 LU01_06 White-tailed … 2022-07-25 11:04:44 7 2022 21429. E4
## 4 LU01 LU01_06 White-tailed … 2022-07-31 06:38:06 7 2022 8356. E5
## 5 LU01 LU01_06 White-tailed … 2022-08-01 09:45:28 8 2022 1627. E6
## 6 LU01 LU01_06 White-tailed … 2022-08-01 15:51:01 8 2022 364. E7Detection plots
Detection data
Now we can apply the same data formatting for each LUs’ data frame using purrr.
We want to count the number of independent detections per species per LU to use in the detection plots
# apply the same formatting to each LU data frame using purrr map
detection_data <- array_frames %>%
purrr::map(
~.x %>%
# group by species
group_by(species) %>%
# calculate a column with unique accounts of each species
mutate(count = n_distinct(event_id)) %>%
# keep just the columns we need
select(species, count) %>%
# keep only unique (distinct) rows so we should be left with one row per species, this helps with plotting later if you don't do it ggplot will try to count and plot each row it's annoying
distinct()) %>%
# set names of list objects
purrr::set_names('Detections LU02',
'Detections LU03',
'Detections LU01',
'Detections LU13',
'Detections LU15',
'Detections LU21')Detection plots
Now to graph independent detections for each LU using purrr, this avoids a TON of code repetition needed to plot each one individually
We use purrr::imap() instead of
purrr::map() because imap maintains the variable names in
our list (e.g. Detections LU01, Detections LU13, etc.) which we can then
use to title each plot.
Within purrr::imap() we just paste the code we would use
for a single ggplot since all the graphical elements (except the title
which we change with the file name [.y]) are the same
# create object detection plots which uses the detection_data list (w/ all 4 LUs)
detection_plots <- detection_data %>%
# use imap instead of map as it allows us to use .y to paste the list element names as the plot titles later
purrr::imap(
~.x %>%
# now just copy and paste the ggplot code for the detection graphs
ggplot(.,
aes(x = reorder(species, count), y = count)) +
# plot as bar graph using geom_col so we don't have to provide a y aesthetic
geom_col() +
# switch the x and y axis
coord_flip() +
# add the number of detections at the end of each bar
geom_text(aes(label = count),
color = "black",
size = 3,
hjust = -0.3,
vjust = 0.2) +
# label x and y axis with informative titles
labs(x = 'Species',
y = 'Number of Independent (30 min) Detections') +
# add title to plot with LU name the .y will take the name of whatever you named each list element in the detection_data list, so make sure this name is what you want on the ggtitle
ggtitle(.y) +
# set the theme
theme_classic() +
theme(plot.title = element_text(hjust = 0.5)))
# view plots, this will print each in it's own window so you have to scroll back in the plot viewer pane to look at each one
detection_plots## $`Detections LU02`
##
## $`Detections LU03`
##
## $`Detections LU01`
##
## $`Detections LU13`
##
## $`Detections LU15`
##
## $`Detections LU21`
Save detection plots
Now we want to save these plots in case we need each individual one (we will combine the detection and naive occ plots into a single figure for each LU later and use those for the OSM report, but we may want these standalone plots later so let’s save them while they are here).
We can save all the plots from the purrr iteration above using
purrr::imap. imap is used instead of map because it allows
us to retain the list object names (plot names) to paste as the file
name with the .y command.
IMPORTANT if you are using this code for a future github repo, DO NOT use .tiff as the file extension. This will cause issues when trying to push any changes to the github repo as the files are too large to meet githubs requirements
# save plots only use if needed
purrr::imap(
detection_plots,
~ggsave(.x,
file = paste0("figures/OSM_",
.y,
'.jpg'), # avoid using .tiff extension in the github repo, those files are too large to push to origin
dpi = 600,
width = 11,
height = 9,
units = 'in'))Naive occupancy
Data
We also need to alter the detection data a bit to use for naive occupancy plots.
We will use the individual LU detection data like we did before and
use purrr::map() to apply the dame data formatting to all 4
data frames.
Here we want to calculate the total number of sites in each LU, the number of sites each species was detected at in each LU and then use both those numbers to calculate naive occupancy for each species in each LU
# First we need to alter the data frame a bit for these plots, let's create a data frame for each LU (I couldn't figure out how to do this without assigning individual data frames for each UGH)
# apply the same formatting to each data frame using purrr
occupancy_data <- array_frames %>%
purrr::map(
~.x %>%
# calculate the total number of sites for each LU
mutate(total_sites = n_distinct(site)) %>%
# group by species to calculate the number of sites each spp occurred at
group_by(species) %>%
# add columns to count the number of sites each spp occurred at and then the naive occupancy
reframe(count = n_distinct(site),
naive_occ = count/total_sites,
ind_det = n_distinct(event_id)) %>%
# keep just the columns we need
select(species, naive_occ, ind_det) %>%
# keep only unique (distinct) rows so we should be left with one row per species, this helps with plotting
distinct()) %>%
purrr::set_names('Naive Occupancy LU02',
'Naive Occupancy LU03',
'Naive Occupancy LU01',
'Naive Occupancy LU13',
'Naive Occupancy LU15',
'Naive Occupancy LU21')Occupancy plots
Now we can graph naive occupancy for each LU using purrr, and as with the detection plots this saves a massive amount of coding using purrr to run an iteration on the data files and produce four plots at once instead of copying and pasting code for each individually
# create object occupancy_plots which uses the occupancy_data list (w/ all 4 LUs)
occupancy_plots <- occupancy_data %>%
# use imap instead of map as it allows us to use .y to paste the list element names as the plot titles later
purrr::imap(
~.x %>%
# now just copy and paste the ggplot code for the occupancy graphs
ggplot(.,
aes(x = fct_reorder(species,
ind_det), # this reorders the species so they match the order of the detection plot which makes it better for viewing when the plots are arranged together in 1 figure for each LU
y = naive_occ)) +
# plot as bars using geom_col() which uses stat = 'identity', instead of geom_bar() which will count the rows in each group and plot that instead of naive occ
geom_col() +
# flip x and y axis
coord_flip() +
# add text to end of bars that provides naive occ value
geom_text(aes(label = round(naive_occ, 2)),
size = 3,
hjust = -0.3,
vjust = 0.2) +
# relabel x and y axis and title
labs(x = 'Species',
y = 'Proportion of Sites With At Least One Detection') +
# set plot title using .y (name of list object)
ggtitle(.y) +
# set. theme elements
theme_classic()+
theme(plot.title = element_text(hjust = 0.5)))
# view plots
occupancy_plots## $`Naive Occupancy LU02`
##
## $`Naive Occupancy LU03`
##
## $`Naive Occupancy LU01`
##
## $`Naive Occupancy LU13`
##
## $`Naive Occupancy LU15`
##
## $`Naive Occupancy LU21`
Save occupancy plots
As with the detection plots, we might want these individual plots
later for something so we can use purrr::imap() to save
them to the figures folder
Again avoid using the .tiff extension in github
# save plots
purrr::imap(
occupancy_plots,
~ggsave(.x,
file = paste0("figures/OSM_",
.y,
'.jpg'), # avoid using .tiff extension in the github repo, those files are too large to push to origin
dpi = 600,
width = 11,
height = 9,
units = 'in'))Final combined plots for report
The previous year’s report had a figure for each LU with the
detections plot on the top and the occupancy plot on the bottom so we
will recreate these for this year using ggarrange().
Unfortunately I could not figure out how to do this in purrr to reduce coding but luckily it isn’t too much repetition
# not sure I know how to do the following section in purrr just yet, but we've saved a ton of coding so far and it doesn't take much to arrange each of these individually
# LU2
LU02_det_occ_plots <- ggarrange(detection_plots$`Detections LU02`,
occupancy_plots$`Naive Occupancy LU02`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU02_det_occ_plots
# LU3
LU03_det_occ_plots <- ggarrange(detection_plots$`Detections LU03`,
occupancy_plots$`Naive Occupancy LU03`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU03_det_occ_plots
# LU1
# arrange the plots so each LU has a figure with detections on top and naive occ on bottom
LU01_det_occ_plots <- ggarrange(detection_plots$`Detections LU01`,
occupancy_plots$`Naive Occupancy LU01`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU01_det_occ_plots
# LU13
# arrange the plots so each LU has a figure with detections on top and naive occ on bottom
LU13_det_occ_plots <- ggarrange(detection_plots$`Detections LU13`,
occupancy_plots$`Naive Occupancy LU13`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU13_det_occ_plots
# LU15
# arrange the plots so each LU has a figure with detections on top and naive occ on bottom
LU15_det_occ_plots <- ggarrange(detection_plots$`Detections LU15`,
occupancy_plots$`Naive Occupancy LU15`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU15_det_occ_plots
# LU21
# arrange the plots so each LU has a figure with detections on top and naive occ on bottom
LU21_det_occ_plots <- ggarrange(detection_plots$`Detections LU21`,
occupancy_plots$`Naive Occupancy LU21`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU21_det_occ_plots
We can however, save all the figures again using purrr
# save all figures at once using purrr
final_det_occ_plots <- list(LU02_det_occ_plots,
LU03_det_occ_plots,
LU01_det_occ_plots,
LU13_det_occ_plots,
LU15_det_occ_plots,
LU21_det_occ_plots) %>%
purrr::set_names('LU02_det_occ_plots',
'LU03_det_occ_plots',
'LU01_det_occ_plots',
'LU13_det_occ_plots',
'LU15_det_occ_plots',
'LU21_det_occ_plots') %>%
purrr::imap(
~ggsave(.x,
file = paste0("figures/OSM_",
.y,
'.jpg'), # avoid using .tiff extension in the github repo, those files are too large to push to origin
dpi = 600,
width = 12,
height = 15,
units = 'in'))Finish with detection data
Before proceeding let’s clear the objects currently in our environment since we don’t need them for the analysis
rm(list = ls(all.names = TRUE))Analysis prep
Now we can start the analysis prep.
First we need to read in the proportional detection (response metrics) and covariate (explanatory metrics) data files for all 6 LUs (fiscal years 2021-2022 and 2022-2023)
Response metrics
Read in data files
We haven’t joined the response metrics from 2021-2022 and 2022-2023 in any previous scripts yet, so let’s read those in using purrr and then join them and take a look at the data to make sure it looks good.
# response metric (proportional detections from the from the ACME_camera_script_9-2-2024.R or .Rmd)
prop_detections <- file.path('data/processed',
c('OSM_proportional_detections_2021.csv',
'OSM_proportional_detections_2022.csv')) %>%
map(~.x %>%
read_csv(.,
# set the column types to read in correctly
col_types = cols(site = col_factor(),
.default = col_number()))) %>%
# give names to each data frame in list
purrr::set_names('prop_dets_2021',
'prop_dets_2022') # R doesn't like when they are just numbers, you can make it work but it's annoying to call the data frame later so I've called them prop_dets_year
# check variable structure
str(prop_detections)## List of 2
## $ prop_dets_2021: spc_tbl_ [78 × 23] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## ..$ site : Factor w/ 78 levels "LU2_03","LU2_05",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ black_bear : num [1:78] 2 2 1 3 2 3 4 3 1 2 ...
## ..$ coyote : num [1:78] 1 0 3 5 6 3 8 0 3 4 ...
## ..$ fisher : num [1:78] 2 0 3 1 0 2 1 1 1 0 ...
## ..$ snowshoe_hare : num [1:78] 1 0 2 5 6 9 14 0 7 3 ...
## ..$ white-tailed_deer : num [1:78] 7 3 6 7 6 7 9 6 5 13 ...
## ..$ cougar : num [1:78] 0 1 0 0 0 0 1 0 0 1 ...
## ..$ lynx : num [1:78] 0 0 3 2 0 4 6 0 1 0 ...
## ..$ red_fox : num [1:78] 0 0 3 0 0 0 0 1 0 0 ...
## ..$ moose : num [1:78] 0 0 0 0 1 0 3 3 0 1 ...
## ..$ grey_wolf : num [1:78] 0 0 0 0 0 0 0 0 1 0 ...
## ..$ caribou : num [1:78] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ absent_black_bear : num [1:78] 3 3 4 2 3 8 7 2 4 9 ...
## ..$ absent_coyote : num [1:78] 6 7 4 2 1 11 6 7 4 10 ...
## ..$ absent_fisher : num [1:78] 5 7 4 6 7 12 13 6 6 14 ...
## ..$ absent_snowshoe_hare : num [1:78] 6 7 5 2 1 5 0 7 0 11 ...
## ..$ absent_white-tailed_deer: num [1:78] 0 4 1 0 1 7 5 1 2 1 ...
## ..$ absent_cougar : num [1:78] 7 6 7 7 7 14 13 7 7 13 ...
## ..$ absent_lynx : num [1:78] 7 7 4 5 7 10 8 7 6 14 ...
## ..$ absent_red_fox : num [1:78] 7 7 4 7 7 14 14 6 7 14 ...
## ..$ absent_moose : num [1:78] 7 7 7 7 6 14 11 4 7 13 ...
## ..$ absent_grey_wolf : num [1:78] 7 7 7 7 7 14 14 7 6 14 ...
## ..$ absent_caribou : num [1:78] 7 7 7 7 7 14 14 7 7 14 ...
## ..- attr(*, "spec")=
## .. .. cols(
## .. .. .default = col_number(),
## .. .. site = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
## .. .. black_bear = col_number(),
## .. .. coyote = col_number(),
## .. .. fisher = col_number(),
## .. .. snowshoe_hare = col_number(),
## .. .. `white-tailed_deer` = col_number(),
## .. .. cougar = col_number(),
## .. .. lynx = col_number(),
## .. .. red_fox = col_number(),
## .. .. moose = col_number(),
## .. .. grey_wolf = col_number(),
## .. .. caribou = col_number(),
## .. .. absent_black_bear = col_number(),
## .. .. absent_coyote = col_number(),
## .. .. absent_fisher = col_number(),
## .. .. absent_snowshoe_hare = col_number(),
## .. .. `absent_white-tailed_deer` = col_number(),
## .. .. absent_cougar = col_number(),
## .. .. absent_lynx = col_number(),
## .. .. absent_red_fox = col_number(),
## .. .. absent_moose = col_number(),
## .. .. absent_grey_wolf = col_number(),
## .. .. absent_caribou = col_number()
## .. .. )
## ..- attr(*, "problems")=<externalptr>
## $ prop_dets_2022: spc_tbl_ [154 × 25] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## ..$ site : Factor w/ 154 levels "LU01_06","LU01_10",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ black_bear : num [1:154] 7 3 4 7 8 9 4 5 7 7 ...
## ..$ coyote : num [1:154] 4 4 8 10 11 9 11 0 9 4 ...
## ..$ fisher : num [1:154] 4 3 3 3 2 1 1 2 0 3 ...
## ..$ moose : num [1:154] 3 2 5 9 1 0 2 4 1 0 ...
## ..$ snowshoe_hare : num [1:154] 4 1 3 0 8 2 2 0 12 4 ...
## ..$ white-tailed_deer : num [1:154] 12 5 12 12 13 14 15 9 12 10 ...
## ..$ cougar : num [1:154] 0 0 1 0 1 0 0 0 0 0 ...
## ..$ grey_wolf : num [1:154] 0 0 2 0 0 0 1 0 0 0 ...
## ..$ lynx : num [1:154] 0 0 1 0 1 1 0 0 0 2 ...
## ..$ red_fox : num [1:154] 0 0 2 0 0 0 0 0 4 0 ...
## ..$ wolverine : num [1:154] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ caribou : num [1:154] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ absent_black_bear : num [1:154] 5 3 8 5 4 3 8 7 5 5 ...
## ..$ absent_coyote : num [1:154] 10 1 6 5 3 5 4 15 6 11 ...
## ..$ absent_fisher : num [1:154] 10 2 11 12 12 13 14 13 15 12 ...
## ..$ absent_moose : num [1:154] 11 3 9 6 13 14 13 11 14 15 ...
## ..$ absent_snowshoe_hare : num [1:154] 10 4 11 15 6 12 13 15 3 11 ...
## ..$ absent_white-tailed_deer: num [1:154] 2 0 2 3 1 0 0 6 3 5 ...
## ..$ absent_cougar : num [1:154] 14 5 13 15 13 14 15 15 15 15 ...
## ..$ absent_grey_wolf : num [1:154] 14 5 12 15 14 14 14 15 15 15 ...
## ..$ absent_lynx : num [1:154] 14 5 13 15 13 13 15 15 15 13 ...
## ..$ absent_red_fox : num [1:154] 14 5 12 15 14 14 15 15 11 15 ...
## ..$ absent_wolverine : num [1:154] 14 5 14 15 14 14 15 15 15 15 ...
## ..$ absent_caribou : num [1:154] 14 5 14 15 14 14 15 15 15 15 ...
## ..- attr(*, "spec")=
## .. .. cols(
## .. .. .default = col_number(),
## .. .. site = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
## .. .. black_bear = col_number(),
## .. .. coyote = col_number(),
## .. .. fisher = col_number(),
## .. .. moose = col_number(),
## .. .. snowshoe_hare = col_number(),
## .. .. `white-tailed_deer` = col_number(),
## .. .. cougar = col_number(),
## .. .. grey_wolf = col_number(),
## .. .. lynx = col_number(),
## .. .. red_fox = col_number(),
## .. .. wolverine = col_number(),
## .. .. caribou = col_number(),
## .. .. absent_black_bear = col_number(),
## .. .. absent_coyote = col_number(),
## .. .. absent_fisher = col_number(),
## .. .. absent_moose = col_number(),
## .. .. absent_snowshoe_hare = col_number(),
## .. .. `absent_white-tailed_deer` = col_number(),
## .. .. absent_cougar = col_number(),
## .. .. absent_grey_wolf = col_number(),
## .. .. absent_lynx = col_number(),
## .. .. absent_red_fox = col_number(),
## .. .. absent_wolverine = col_number(),
## .. .. absent_caribou = col_number()
## .. .. )
## ..- attr(*, "problems")=<externalptr>Merge data files
Now we need to merge the two data files for analysis. We can do this with dplyr.
# merge the proportional detections files so there are rows for both fiscal years
prop_dets_all <- dplyr::bind_rows(prop_detections$prop_dets_2021,
prop_detections$prop_dets_2022)
print(prop_dets_all)## # A tibble: 232 × 25
## site black_bear coyote fisher snowshoe_hare `white-tailed_deer` cougar lynx
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 LU2_… 2 1 2 1 7 0 0
## 2 LU2_… 2 0 0 0 3 1 0
## 3 LU2_… 1 3 3 2 6 0 3
## 4 LU2_… 3 5 1 5 7 0 2
## 5 LU2_… 2 6 0 6 6 0 0
## 6 LU2_… 3 3 2 9 7 0 4
## 7 LU2_… 4 8 1 14 9 1 6
## 8 LU2_… 3 0 1 0 6 0 0
## 9 LU2_… 1 3 1 7 5 0 1
## 10 LU2_… 2 4 0 3 13 1 0
## # ℹ 222 more rows
## # ℹ 17 more variables: red_fox <dbl>, moose <dbl>, grey_wolf <dbl>,
## # caribou <dbl>, absent_black_bear <dbl>, absent_coyote <dbl>,
## # absent_fisher <dbl>, absent_snowshoe_hare <dbl>,
## # `absent_white-tailed_deer` <dbl>, absent_cougar <dbl>, absent_lynx <dbl>,
## # absent_red_fox <dbl>, absent_moose <dbl>, absent_grey_wolf <dbl>,
## # absent_caribou <dbl>, wolverine <dbl>, absent_wolverine <dbl>Format data
This looks good except since there were no wolverines in the first fiscal year of monitoring (LU02 and LU03) those columns have NAs for both arrays, we want to replace those NAs with Zeros and move the wolverine column to the correct location
Let’s do that now.
prop_dets_all <- prop_dets_all %>%
# replace NAs introduced from joining data to zeros
replace(is.na(.),
0) %>%
# relocate wolverine column
relocate(.,
wolverine,
.after = caribou)
# check data
head(prop_dets_all)## # A tibble: 6 × 25
## site black_bear coyote fisher snowshoe_hare `white-tailed_deer` cougar lynx
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 LU2_03 2 1 2 1 7 0 0
## 2 LU2_05 2 0 0 0 3 1 0
## 3 LU2_1… 1 3 3 2 6 0 3
## 4 LU2_1… 3 5 1 5 7 0 2
## 5 LU2_1… 2 6 0 6 6 0 0
## 6 LU2_1… 3 3 2 9 7 0 4
## # ℹ 17 more variables: red_fox <dbl>, moose <dbl>, grey_wolf <dbl>,
## # caribou <dbl>, wolverine <dbl>, absent_black_bear <dbl>,
## # absent_coyote <dbl>, absent_fisher <dbl>, absent_snowshoe_hare <dbl>,
## # `absent_white-tailed_deer` <dbl>, absent_cougar <dbl>, absent_lynx <dbl>,
## # absent_red_fox <dbl>, absent_moose <dbl>, absent_grey_wolf <dbl>,
## # absent_caribou <dbl>, absent_wolverine <dbl>Looks good!
Save data
Let’s save the merged and formatted detection data from 2021 and 2022 for future use
write_csv(prop_dets_all,
'data/processed/OSM_proportional_detections_merged_2021_2022.csv')Covariates
In the previous script, 2_ACME_landscape_covariate_exploration_script.Rmd we joined the two fiscal years of data and grouped the covariates for analysis and saved this data as a c.sv file, so we can read in this file now and we shouldn’t have to do any further formatting at the moment
Read data
We will check the data structure after reading in the file just to make sure everything looks good.
covariates_all <- read_csv('data/processed/OSM_covariates_grouped_2021_2022.csv',
# set the column types to read in correctly
col_types = cols(array = col_factor(),
camera = col_factor(),
site = col_factor(),
buff_dist = col_factor(),
.default = col_number()))## Warning: The following named parsers don't match the column names: camerastr(covariates_all)## spc_tbl_ [4,660 × 19] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ array : Factor w/ 6 levels "LU13","LU15",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ site : Factor w/ 233 levels "LU13_18","LU13_15",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ buff_dist : Factor w/ 20 levels "250","500","750",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ harvest : num [1:4660] 0 0 0.687 0.337 0 ...
## $ pipeline : num [1:4660] 0 0.068 0 0 0.0301 ...
## $ roads : num [1:4660] 0 0.0174 0 0 0 ...
## $ seismic_lines : num [1:4660] 0 0.03277 0 0.00889 0.01144 ...
## $ seismic_lines_3D : num [1:4660] 0 0 0 0 0.0523 ...
## $ trails : num [1:4660] 0.00588 0.0028 0 0.01591 0 ...
## $ transmission_lines: num [1:4660] 0.0642 0 0 0 0.091 ...
## $ veg_edges : num [1:4660] 0 0.0858 0 0 0 ...
## $ wells : num [1:4660] 0 0 0 0 0.0322 ...
## $ lc_grassland : num [1:4660] 0.193 0.348 0 0 0.178 ...
## $ lc_coniferous : num [1:4660] 0.456 0.358 0.186 1 0.822 ...
## $ lc_broadleaf : num [1:4660] 0 0 0 0 0 ...
## $ lc_mixed : num [1:4660] 0 0.101 0.255 0 0 ...
## $ lc_developed : num [1:4660] 0 0.0916 0 0 0 ...
## $ lc_shrub : num [1:4660] 0.316 0 0.559 0 0 ...
## $ osm_industrial : num [1:4660] 0.383 0.157 0 0 0 ...
## - attr(*, "spec")=
## .. cols(
## .. .default = col_number(),
## .. array = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
## .. site = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
## .. buff_dist = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
## .. harvest = col_number(),
## .. pipeline = col_number(),
## .. roads = col_number(),
## .. seismic_lines = col_number(),
## .. seismic_lines_3D = col_number(),
## .. trails = col_number(),
## .. transmission_lines = col_number(),
## .. veg_edges = col_number(),
## .. wells = col_number(),
## .. lc_grassland = col_number(),
## .. lc_coniferous = col_number(),
## .. lc_broadleaf = col_number(),
## .. lc_mixed = col_number(),
## .. lc_developed = col_number(),
## .. lc_shrub = col_number(),
## .. osm_industrial = col_number()
## .. )
## - attr(*, "problems")=<externalptr>Everything looks good!
###Subset data by buffer
We need to subset the data so we have separate data frames for each buffer width to work with in the analysis AND to explore correlation between variables at each buffer width, as these may very with spatial scales
Let’s use a for loop to subset the data
buffer_frames <- list()
for (i in unique(covariates_all$buff_dist)){
print(i)
# Subset data based on radius
df <- covariates_all %>%
filter(buff_dist == i)
# list of dataframes
buffer_frames <-c (buffer_frames, list(df))
}## [1] "250"
## [1] "500"
## [1] "750"
## [1] "1000"
## [1] "1250"
## [1] "1500"
## [1] "1750"
## [1] "2000"
## [1] "2250"
## [1] "2500"
## [1] "2750"
## [1] "3000"
## [1] "3250"
## [1] "3500"
## [1] "3750"
## [1] "4000"
## [1] "4250"
## [1] "4500"
## [1] "4750"
## [1] "5000"# name list objects so we can extract names for plotting
buffer_frames <- buffer_frames %>%
# absurdly long way to do this but for sake of time fuck it
purrr::set_names('250 meter buffer',
'500 meter buffer',
'750 meter buffer',
'1000 meter buffer',
'1250 meter buffer',
'1500 meter buffer',
'1750 meter buffer',
'2000 meter buffer',
'2250 meter buffer',
'2500 meter buffer',
'2750 meter buffer',
'3000 meter buffer',
'3250 meter buffer',
'3500 meter buffer',
'3750 meter buffer',
'4000 meter buffer',
'4250 meter buffer',
'4500 meter buffer',
'4750 meter buffer',
'5000 meter buffer')Now we have a list with data frames for each buffer width which we can work with later.
Add response metric
Now that we have the covariate data formatted we need to add the response metric (monthly proportional presence/absence) to the data frames
osm_final_df_2021_2022 <- buffer_frames %>%
purrr::map(
~.x %>%
left_join(prop_dets_all,
by = 'site'))Finish with data formatting
Let’s remove the objects we no longer need from the environment to keep our workspace clean
rm(covariates_all,
prop_detections,
df,
i)Analysis
Now we are going to run a global model which includes all HFI and LC variables that at first glance (will do a more thorough check later) seem to have enough data to include as covariates for each buffer width, and then we will compare these models see which buffer width best fit the data for each species. After that we will optimize models so they don’t includes any variables that are highly correlated.
We don’t need to do ALL the species since many don’t have enough data.
Refer to the 1_ACME_camera_script_9-2-2024.html or .Rmd the plot for proportional monthly detections should provide info on which species we have enough data for, can be found under Response metrics/3.Proportion monthly detections
A brief look at this fig indicates that we have enough for all the mammals in the prop_detections data frame except
- cougar
- wolverine
- caribou??? (may have enough, may not)
Black bear
there is probably a way to shorten the following code to select particular species, I saw Andrew’s for loop in the draft script he wrote but couldn’t quite figure out how to adapt it to my purposes with the data formatted the way I have it, so I did this instead, maybe we can merge approaches later to clean this up if deemed necessary? But it certainly functions for now and is understandable… I think.
Global models
Let’s start with bears and use purrr to create a global model for every buffer distance
Recall purrr::map() is magical for iterations and will
apply all the functions within the map() function to each
item of the list supplied before the the map()
function.
# create models for black bears at each buffer size
black_bear_mods <- osm_final_df_2021_2022 %>%
# use purrr map to run the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(black_bear, absent_black_bear) ~
# HFI
scale(harvest) +
scale(pipeline) +
scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(transmission_lines) +
scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
# VEG covariates in numerical order
scale(lc_grassland) +
scale(lc_coniferous) +
scale(lc_broadleaf) +
scale(lc_mixed) +
scale(lc_developed) +
scale(lc_shrub) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))Model selection
We will use the model.sel() function from the
MuMIn package to compare the global models for each buffer
width and see which buffer fits the black bear data best
model.sel(black_bear_mods)## Model selection table
## cnd((Int)) dsp((Int)) cnd(scl(hrv)) cnd(scl(lc_brd))
## 250 meter buffer -0.5998 + 0.012140 0.1221
## 1750 meter buffer -0.5987 + 0.008720 0.3954
## 2000 meter buffer -0.5976 + 0.005699 0.4518
## 1250 meter buffer -0.5996 + 0.008946 0.3455
## 1500 meter buffer -0.6002 + 0.016130 0.3382
## 1000 meter buffer -0.5983 + -0.001997 0.4015
## 5000 meter buffer -0.6074 + 0.047290 0.1153
## 2250 meter buffer -0.5960 + -0.001043 0.4491
## 2750 meter buffer -0.5959 + 0.005900 0.4160
## 2500 meter buffer -0.5954 + 0.001508 0.4295
## 3750 meter buffer -0.6021 + 0.024810 0.3098
## 4000 meter buffer -0.6037 + 0.035760 0.3021
## 3500 meter buffer -0.6004 + 0.023550 0.3449
## 3250 meter buffer -0.5989 + 0.024520 0.3668
## 4750 meter buffer -0.6059 + 0.037070 0.1895
## 4250 meter buffer -0.6040 + 0.040600 0.2960
## 3000 meter buffer -0.5967 + 0.011800 0.3862
## 500 meter buffer -0.6006 + 0.029270 0.2784
## 4500 meter buffer -0.6048 + 0.037590 0.2450
## 750 meter buffer -0.5978 + 0.022290 0.3135
## cnd(scl(lc_cnf)) cnd(scl(lc_dvl)) cnd(scl(lc_grs))
## 250 meter buffer 0.01706 -0.086020 0.067700
## 1750 meter buffer 0.28860 -0.149500 -0.001904
## 2000 meter buffer 0.37870 -0.137500 0.004301
## 1250 meter buffer 0.18040 -0.146700 -0.007598
## 1500 meter buffer 0.18560 -0.166800 -0.010420
## 1000 meter buffer 0.25690 -0.112100 0.021270
## 5000 meter buffer -0.05003 -0.101400 0.093600
## 2250 meter buffer 0.39070 -0.153800 -0.004265
## 2750 meter buffer 0.35480 -0.169300 0.014090
## 2500 meter buffer 0.37300 -0.152200 -0.006222
## 3750 meter buffer 0.17330 -0.159600 0.034770
## 4000 meter buffer 0.15840 -0.140500 0.061200
## 3500 meter buffer 0.22620 -0.155000 0.028350
## 3250 meter buffer 0.27570 -0.156100 0.024090
## 4750 meter buffer 0.03390 -0.103500 0.088520
## 4250 meter buffer 0.14460 -0.117100 0.074410
## 3000 meter buffer 0.31280 -0.166900 0.015930
## 500 meter buffer 0.15610 -0.001471 0.108800
## 4500 meter buffer 0.08703 -0.113800 0.082690
## 750 meter buffer 0.18220 -0.048030 0.044580
## cnd(scl(lc_mxd)) cnd(scl(lc_shr)) cnd(scl(osm_ind))
## 250 meter buffer 0.054090 0.11220 0.046220
## 1750 meter buffer 0.150400 0.17960 -0.023060
## 2000 meter buffer 0.195400 0.22580 -0.030690
## 1250 meter buffer 0.133300 0.15110 0.026870
## 1500 meter buffer 0.114200 0.14190 0.004238
## 1000 meter buffer 0.184100 0.17820 -0.002263
## 5000 meter buffer 0.008365 0.04685 -0.077250
## 2250 meter buffer 0.205400 0.23040 -0.045190
## 2750 meter buffer 0.189300 0.21420 -0.071020
## 2500 meter buffer 0.197600 0.22710 -0.056540
## 3750 meter buffer 0.093200 0.14440 -0.106300
## 4000 meter buffer 0.078050 0.13870 -0.085970
## 3500 meter buffer 0.112300 0.16360 -0.106600
## 3250 meter buffer 0.133400 0.17710 -0.106000
## 4750 meter buffer 0.044460 0.08694 -0.084660
## 4250 meter buffer 0.071410 0.14580 -0.070480
## 3000 meter buffer 0.167100 0.19230 -0.090640
## 500 meter buffer 0.114000 0.14040 0.023410
## 4500 meter buffer 0.056320 0.11480 -0.080600
## 750 meter buffer 0.138500 0.11560 0.035930
## cnd(scl(ppl)) cnd(scl(rds)) cnd(scl(ssm_lns_3D))
## 250 meter buffer 4.783e-02 -0.104000 -0.11560
## 1750 meter buffer -7.498e-02 0.158300 -0.11170
## 2000 meter buffer -9.514e-02 0.176000 -0.10060
## 1250 meter buffer 9.031e-03 -0.006562 -0.13600
## 1500 meter buffer -2.918e-02 0.073920 -0.12820
## 1000 meter buffer -9.271e-03 0.017020 -0.12980
## 5000 meter buffer -4.227e-01 0.005939 -0.08420
## 2250 meter buffer -9.128e-02 0.202100 -0.09823
## 2750 meter buffer -1.476e-01 0.273700 -0.08796
## 2500 meter buffer -1.030e-01 0.245000 -0.09330
## 3750 meter buffer -1.610e-01 0.117700 -0.08006
## 4000 meter buffer -2.101e-01 0.062950 -0.06989
## 3500 meter buffer -1.497e-01 0.174000 -0.07614
## 3250 meter buffer -1.592e-01 0.241900 -0.07557
## 4750 meter buffer -3.426e-01 0.037880 -0.07104
## 4250 meter buffer -2.148e-01 0.031390 -0.06951
## 3000 meter buffer -1.587e-01 0.273800 -0.08137
## 500 meter buffer 7.892e-05 -0.133900 -0.10010
## 4500 meter buffer -2.709e-01 0.042870 -0.06702
## 750 meter buffer 2.574e-02 -0.074510 -0.12570
## cnd(scl(ssm_lns)) cnd(scl(trl)) cnd(scl(trn_lns))
## 250 meter buffer -0.03550 0.10260 -0.129200
## 1750 meter buffer -0.18240 0.07213 -0.092160
## 2000 meter buffer -0.17810 0.07224 -0.078630
## 1250 meter buffer -0.13410 0.08228 -0.128800
## 1500 meter buffer -0.16190 0.07507 -0.114800
## 1000 meter buffer -0.12410 0.09118 -0.114300
## 5000 meter buffer -0.13330 0.01758 0.224000
## 2250 meter buffer -0.17130 0.07448 -0.093590
## 2750 meter buffer -0.16380 0.05180 -0.067080
## 2500 meter buffer -0.16810 0.06390 -0.086760
## 3750 meter buffer -0.15790 0.07173 -0.004969
## 4000 meter buffer -0.15410 0.06589 0.029670
## 3500 meter buffer -0.15900 0.06431 -0.033120
## 3250 meter buffer -0.15790 0.05717 -0.044450
## 4750 meter buffer -0.14610 0.03566 0.154500
## 4250 meter buffer -0.16110 0.05966 0.052200
## 3000 meter buffer -0.15550 0.04647 -0.055050
## 500 meter buffer -0.04670 0.05432 -0.104000
## 4500 meter buffer -0.15530 0.05068 0.096340
## 750 meter buffer -0.06456 0.04651 -0.107200
## cnd(scl(veg_edg)) cnd(scl(wll)) df logLik AICc delta
## 250 meter buffer -0.009170 -0.0136 18 -443.782 926.8 0.00
## 1750 meter buffer -0.047050 0.1401 18 -444.092 927.4 0.62
## 2000 meter buffer -0.062020 0.1507 18 -444.158 927.5 0.75
## 1250 meter buffer 0.033960 0.1370 18 -444.226 927.7 0.89
## 1500 meter buffer -0.008584 0.1543 18 -444.304 927.8 1.04
## 1000 meter buffer 0.027230 0.1196 18 -444.336 927.9 1.11
## 5000 meter buffer -0.102300 0.4047 18 -444.816 928.8 2.07
## 2250 meter buffer -0.058860 0.1602 18 -445.030 929.3 2.50
## 2750 meter buffer -0.099740 0.1762 18 -445.409 930.0 3.25
## 2500 meter buffer -0.093760 0.1587 18 -445.417 930.0 3.27
## 3750 meter buffer -0.052440 0.2391 18 -445.548 930.3 3.53
## 4000 meter buffer -0.053510 0.2714 18 -445.553 930.3 3.54
## 3500 meter buffer -0.069670 0.2051 18 -445.859 930.9 4.15
## 3250 meter buffer -0.096130 0.1884 18 -445.949 931.1 4.33
## 4750 meter buffer -0.086190 0.3327 18 -446.118 931.4 4.67
## 4250 meter buffer -0.062220 0.2681 18 -446.193 931.6 4.82
## 3000 meter buffer -0.100500 0.1798 18 -446.234 931.7 4.90
## 500 meter buffer 0.024600 0.0979 18 -446.296 931.8 5.03
## 4500 meter buffer -0.073450 0.2919 18 -446.364 931.9 5.16
## 750 meter buffer -0.003772 0.1360 18 -447.921 935.1 8.28
## weight
## 250 meter buffer 0.159
## 1750 meter buffer 0.117
## 2000 meter buffer 0.109
## 1250 meter buffer 0.102
## 1500 meter buffer 0.094
## 1000 meter buffer 0.091
## 5000 meter buffer 0.057
## 2250 meter buffer 0.046
## 2750 meter buffer 0.031
## 2500 meter buffer 0.031
## 3750 meter buffer 0.027
## 4000 meter buffer 0.027
## 3500 meter buffer 0.020
## 3250 meter buffer 0.018
## 4750 meter buffer 0.015
## 4250 meter buffer 0.014
## 3000 meter buffer 0.014
## 500 meter buffer 0.013
## 4500 meter buffer 0.012
## 750 meter buffer 0.003
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)Looks like the smallest buffer (250) fits the data best for black bears.
Let’s look at this model closer
Model summary
summary(black_bear_mods$`250 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(black_bear, absent_black_bear) ~ scale(harvest) + scale(pipeline) +
## scale(roads) + scale(seismic_lines) + scale(seismic_lines_3D) +
## scale(trails) + scale(transmission_lines) + scale(veg_edges) +
## scale(wells) + scale(osm_industrial) + scale(lc_grassland) +
## scale(lc_coniferous) + scale(lc_broadleaf) + scale(lc_mixed) +
## scale(lc_developed) + scale(lc_shrub) + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 923.6 985.6 -443.8 887.6 214
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.05453 0.2335
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.59976 0.10675 -5.618 1.93e-08 ***
## scale(harvest) 0.01214 0.05072 0.239 0.8109
## scale(pipeline) 0.04783 0.07103 0.673 0.5007
## scale(roads) -0.10401 0.09070 -1.147 0.2515
## scale(seismic_lines) -0.03550 0.05305 -0.669 0.5034
## scale(seismic_lines_3D) -0.11563 0.05881 -1.966 0.0493 *
## scale(trails) 0.10263 0.04771 2.151 0.0315 *
## scale(transmission_lines) -0.12923 0.06773 -1.908 0.0564 .
## scale(veg_edges) -0.00917 0.07788 -0.118 0.9063
## scale(wells) -0.01360 0.05402 -0.252 0.8012
## scale(osm_industrial) 0.04622 0.05292 0.873 0.3825
## scale(lc_grassland) 0.06770 0.09942 0.681 0.4959
## scale(lc_coniferous) 0.01706 0.26332 0.065 0.9483
## scale(lc_broadleaf) 0.12207 0.18033 0.677 0.4985
## scale(lc_mixed) 0.05409 0.15802 0.342 0.7321
## scale(lc_developed) -0.08602 0.09897 -0.869 0.3847
## scale(lc_shrub) 0.11218 0.14259 0.787 0.4314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Nothing looks fishy in the model summary for now, we will look at this more closely once we have a true top model.
Code to remove a model
At one point the 250 meter buffer was giving us issues so we had to remove it. After re-extracting the data and re-doing some data formatting this is no longer an issue but I’ve saved the code here in case it’s needed in the future
# create models for black bears at each buffer size
black_bear_mods_no250 <- osm_final_df_2021_2022 %>%
# remove 250 meter buffer width
purrr::discard_at('250 meter buffer') %>%
# use purrr map to fun the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(black_bear, absent_black_bear) ~
# HFI
scale(harvest) +
scale(pipeline) +
scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(transmission_lines) +
scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
# VEG covariates in numerical order
scale(lc_grassland) +
scale(lc_coniferous) +
scale(lc_broadleaf) +
scale(lc_mixed) +
scale(lc_developed) +
scale(lc_shrub) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))Subset models
Autocorrelation 250m
Before we can develop model subsets we need to see what variables can be included in the same model at this buffer width.
Let’s use the chart.Correlation() function in the
Performance Analytics package to look at this.
buffer_frames$`250 meter buffer` %>%
select_if(is.numeric) %>%
# use chart.correlation
chart.Correlation(.,
histogram = TRUE,
method = "pearson")
mtext('250 meter buffer', side = 3, line = 3)
> You can click on this fig to zoom in!
List of correlated variables:
- pipeline & transmission_lines 0.53
- roads & veg_edges 0.71
- roads & lc_developed 0.57
Model list (Andrew can you review this)
Jake/Andrew can you suggest some additional subset models if needed
Let’s create another global model without these correlated variables. I’m going to select transmission_lines over pipeline because the summary from earlier showed transmission lines had larger effect on black bear presence, and I’m going to choose to keep roads instead of veg edges and the developed landcover class because we are interested in the effect of roads more than these other two variables.
# global model w/ non-correlated variables
bear_global_250 <- glmmTMB::glmmTMB(cbind(black_bear, absent_black_bear) ~
# HFI
scale(harvest) +
scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(transmission_lines) +
scale(wells) +
scale(osm_industrial) +
# VEG covariates in numerical order
scale(lc_grassland) +
scale(lc_coniferous) +
scale(lc_broadleaf) +
scale(lc_mixed) +
scale(lc_shrub) +
# Random effect of array
(1|array),
data = osm_final_df_2021_2022$`250 meter buffer`,
family = 'binomial')
# null model to compare
bear_null_250 <- glmmTMB::glmmTMB(cbind(black_bear, absent_black_bear) ~ 1 +
# Random effect of array
(1|array),
data = osm_final_df_2021_2022$`250 meter buffer`,
family = 'binomial')
# second model is based on linear features providing easier movement through boreal forest
bear_linear_250 <- glmmTMB::glmmTMB(cbind(black_bear, absent_black_bear) ~
# HFI
scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(transmission_lines) +
# Random effect of array
(1|array),
data = osm_final_df_2021_2022$`250 meter buffer`,
family = 'binomial')Model selection
Now let’s look at a model selection table with our subset models.
model.sel(bear_global_250,
bear_null_250,
bear_linear_250)## Model selection table
## cnd((Int)) dsp((Int)) cnd(scl(hrv)) cnd(scl(lc_brd))
## bear_linear_250 -0.5986 +
## bear_global_250 -0.5995 + 0.01223 0.2326
## bear_null_250 -0.5884 +
## cnd(scl(lc_cnf)) cnd(scl(lc_grs)) cnd(scl(lc_mxd))
## bear_linear_250
## bear_global_250 0.1823 0.1248 0.1455
## bear_null_250
## cnd(scl(lc_shr)) cnd(scl(osm_ind)) cnd(scl(rds))
## bear_linear_250 -0.1586
## bear_global_250 0.1891 0.04922 -0.1215
## bear_null_250
## cnd(scl(ssm_lns_3D)) cnd(scl(ssm_lns)) cnd(scl(trl))
## bear_linear_250 -0.1311 -0.04436 0.10250
## bear_global_250 -0.1135 -0.04271 0.09811
## bear_null_250
## cnd(scl(trn_lns)) cnd(scl(wll)) df logLik AICc delta weight
## bear_linear_250 -0.09901 7 -449.674 913.8 0.00 0.972
## bear_global_250 -0.10520 -0.01348 15 -444.383 921.0 7.14 0.027
## bear_null_250 2 -463.090 930.2 16.38 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)Looks like the linear feature model is best, SO FAR
Top model
250 meter buffer - linear features
Summary
summary(bear_linear_250)## Family: binomial ( logit )
## Formula:
## cbind(black_bear, absent_black_bear) ~ scale(roads) + scale(seismic_lines) +
## scale(seismic_lines_3D) + scale(trails) + scale(transmission_lines) +
## (1 | array)
## Data: osm_final_df_2021_2022$`250 meter buffer`
##
## AIC BIC logLik deviance df.resid
## 913.3 937.5 -449.7 899.3 225
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.05426 0.2329
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.59856 0.10644 -5.624 1.87e-08 ***
## scale(roads) -0.15864 0.05694 -2.786 0.00534 **
## scale(seismic_lines) -0.04436 0.05006 -0.886 0.37556
## scale(seismic_lines_3D) -0.13114 0.05635 -2.327 0.01995 *
## scale(trails) 0.10247 0.04641 2.208 0.02724 *
## scale(transmission_lines) -0.09901 0.05653 -1.751 0.07987 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Odds ratios
Let’s extract the odds ratios for the top model so we can plot them for data vis later.
bear_model_odds <- bear_linear_250 %>%
# extract the coefficients and upper and lower CI
confint() %>%
# format resulting object as a tibble data frame
as_tibble() %>%
# add a column where we can put the feature names
rowid_to_column() %>%
# rename the columns for plotting
rename('lower' = `2.5 %`,
'upper' = `97.5 %`,
'estimate' = Estimate,
'feature' = rowid) %>%
# rename the entries to features, need to look at the order the features are in from the model summary and ensure it matches
mutate(feature = as.factor(feature),
feature = recode(feature,
'1' = 'intercept',
'2' = 'roads',
'3' = 'seismic_lines',
'4' = 'seismic_lines_3D',
'5' = 'trails',
'6' = 'transmission_lines',
'7' = 'intercept_array')) %>%
# remove intercepts
filter(!grepl('intercept',
feature))Plot odds ratios
First let’s get a silhouette for this graphy from phylopic
black_bear_img <- get_phylopic(get_uuid(name = 'Ursus americanus'))Now let’s use ggplot to plot the odds ratios for each feature in the top model
# provide data and mapping aesthetics
ggplot(bear_model_odds, aes(x = feature,
y = estimate)) +
geom_errorbar(aes(ymin = lower,
ymax = upper),
width = 0.4,
linewidth = 0.5,
position = position_dodge(width = 0.9)) +
geom_hline(yintercept = 0, linetype = "dashed") +
labs(y = 'Odds ratio') +
scale_x_discrete(labels = c('Roads',
'Seismic Lines',
'3D Seismic Lines',
'Trails',
'Transmission Lines')) +
add_phylopic(black_bear_img,
x = 5.3,
y = 0.2,
ysize = 0.05) +
theme_classic() +
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 90,
hjust = 1),
axis.title = element_text(size = 16),
axis.text = element_text(size = 14))
Let’s repeat this process for each species that we have enough data for.
Caribou
We may or may not have enough data for caribou but let’s try it at least for this preliminary report
We can use the same code from black bears (above) to run global models for each buffer width except remember we want to remove 250 meters
And in the same chunk to save time let’s also run the
model.sel() function
Global models
caribou_mods <- osm_final_df_2021_2022 %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(caribou, absent_caribou) ~
# HFI
scale(harvest) +
scale(pipeline) +
scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(transmission_lines) +
scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
# VEG covariates in numerical order
scale(lc_grassland) +
scale(lc_coniferous) +
scale(lc_broadleaf) +
scale(lc_mixed) +
scale(lc_developed) +
scale(lc_shrub) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')
## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')
## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')
## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')
## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')# model selection
model.sel(caribou_mods)## Model selection table
## cnd((Int)) dsp((Int)) cnd(scl(hrv)) cnd(scl(lc_brd))
## 3000 meter buffer -8.327 + 0.25940 2.3660
## 3250 meter buffer -8.413 + 0.40820 4.7700
## 2500 meter buffer -7.922 + 0.02584 -0.2111
## 3500 meter buffer -8.443 + 0.58490 5.7480
## 2750 meter buffer -7.879 + 0.11420 1.0120
## 1750 meter buffer -7.391 + -0.05851 1.9440
## 2250 meter buffer -7.581 + 0.06022 0.0175
## 2000 meter buffer -7.068 + -0.09839 0.3358
## 1500 meter buffer -7.190 + -0.14750 0.8697
## 3750 meter buffer -7.991 + 0.68070 6.2590
## 1250 meter buffer -71.530 + -0.24830 -0.8018
## 5000 meter buffer -7.286 + -0.21240 6.0100
## 4000 meter buffer -7.325 + 0.84700 6.9050
## 4750 meter buffer -7.182 + 0.34290 7.0820
## 4500 meter buffer -7.264 + 0.96420 7.9520
## 4250 meter buffer -7.112 + 0.93660 7.1020
## 1000 meter buffer -121.300 + -0.88000 -1.4550
## 750 meter buffer -43.960 + -1.42900 -1.6690
## 500 meter buffer -840.800 + -2.41900 -1.4380
## 250 meter buffer -206.200 + -421.40000 -2.7170
## cnd(scl(lc_cnf)) cnd(scl(lc_dvl)) cnd(scl(lc_grs))
## 3000 meter buffer 10.3800 3.4700 1.2090
## 3250 meter buffer 12.5800 3.6880 1.6250
## 2500 meter buffer 6.9770 2.5380 0.4845
## 3500 meter buffer 12.5000 3.8100 1.6340
## 2750 meter buffer 8.3090 2.9310 0.7919
## 1750 meter buffer 9.4270 2.1820 1.0480
## 2250 meter buffer 6.6580 2.1220 0.4970
## 2000 meter buffer 6.4720 1.9540 0.5902
## 1500 meter buffer 7.0280 1.3290 0.8263
## 3750 meter buffer 12.0400 3.3390 1.5220
## 1250 meter buffer 3.1020 0.1831 0.3333
## 5000 meter buffer 12.0500 2.1740 1.7240
## 4000 meter buffer 12.0400 2.2370 1.4110
## 4750 meter buffer 12.4500 1.5170 1.6060
## 4500 meter buffer 13.2500 1.4420 1.6180
## 4250 meter buffer 12.1800 1.5420 1.4130
## 1000 meter buffer 0.5239 -0.1699 -0.1059
## 750 meter buffer -0.7779 -0.2413 -0.3634
## 500 meter buffer -0.5981 -0.5873 -0.3747
## 250 meter buffer -0.9887 -0.9365 -0.5740
## cnd(scl(lc_mxd)) cnd(scl(lc_shr)) cnd(scl(osm_ind))
## 3000 meter buffer 4.58300 5.10400 0.04095
## 3250 meter buffer 4.75200 6.13000 -0.07332
## 2500 meter buffer 3.41100 3.49100 -0.30210
## 3500 meter buffer 3.81300 6.00900 -0.07073
## 2750 meter buffer 3.85900 4.12200 -0.02226
## 1750 meter buffer 4.86600 4.94700 -0.49040
## 2250 meter buffer 3.07400 3.33900 -0.69810
## 2000 meter buffer 3.25200 3.35000 -0.60850
## 1500 meter buffer 3.56400 3.61300 -0.53180
## 3750 meter buffer 3.16900 5.75500 -0.02580
## 1250 meter buffer 1.25600 1.42400 -0.97300
## 5000 meter buffer 3.74900 6.21200 1.64700
## 4000 meter buffer 3.21900 5.96400 -0.05433
## 4750 meter buffer 3.45400 6.38300 1.05400
## 4500 meter buffer 3.57200 6.74800 0.16230
## 4250 meter buffer 3.30100 6.14000 -0.04194
## 1000 meter buffer -0.05488 -0.02029 -1.44600
## 750 meter buffer -0.75030 -0.73140 -0.97290
## 500 meter buffer -0.52240 -0.60790 -0.24610
## 250 meter buffer -0.67780 -0.72120 -0.08231
## cnd(scl(ppl)) cnd(scl(rds)) cnd(scl(ssm_lns_3D))
## 3000 meter buffer -0.87190 1.11100 0.058070
## 3250 meter buffer -1.06100 1.73800 0.408500
## 2500 meter buffer -0.02702 -0.22180 -0.172100
## 3500 meter buffer -1.31300 1.85600 0.596900
## 2750 meter buffer -0.45420 0.30130 -0.147300
## 1750 meter buffer 0.91290 -0.64880 -0.005105
## 2250 meter buffer 0.48070 -0.10070 -0.096940
## 2000 meter buffer 0.50400 -0.32360 -0.039980
## 1500 meter buffer 0.97430 -0.10050 0.011860
## 3750 meter buffer -1.35300 1.67200 0.751900
## 1250 meter buffer 0.59150 0.61250 0.047650
## 5000 meter buffer -0.39520 -1.02000 0.284300
## 4000 meter buffer -0.73410 0.96520 0.631600
## 4750 meter buffer -0.38340 -0.41850 0.277700
## 4500 meter buffer -0.39080 0.72670 0.413400
## 4250 meter buffer -0.52920 0.99310 0.467600
## 1000 meter buffer 0.51580 0.77400 0.024020
## 750 meter buffer 0.42510 0.10420 -0.097430
## 500 meter buffer 0.16950 0.32310 -0.199400
## 250 meter buffer 0.07895 0.05935 -0.291100
## cnd(scl(ssm_lns)) cnd(scl(trl)) cnd(scl(trn_lns))
## 3000 meter buffer 0.36310 -0.202000 -1.7600
## 3250 meter buffer 0.49490 -0.291400 -2.0620
## 2500 meter buffer 0.43350 0.006096 -1.0940
## 3500 meter buffer 0.68140 -0.388600 -2.2220
## 2750 meter buffer 0.38360 -0.083580 -1.2860
## 1750 meter buffer -0.14810 0.010880 -1.7330
## 2250 meter buffer 0.45640 0.147300 -1.1670
## 2000 meter buffer -0.03146 0.064910 -1.4170
## 1500 meter buffer -0.08270 0.060250 -2.3370
## 3750 meter buffer 0.68810 -0.366600 -1.8540
## 1250 meter buffer 0.22600 0.245600 -146.9000
## 5000 meter buffer 0.33590 -0.334100 -0.8979
## 4000 meter buffer 0.52040 -0.324100 -1.0870
## 4750 meter buffer 0.54200 -0.384300 -0.5912
## 4500 meter buffer 0.57490 -0.332600 -0.5100
## 4250 meter buffer 0.51920 -0.347700 -0.6046
## 1000 meter buffer 0.20020 0.245300 -274.0000
## 750 meter buffer 0.25540 0.159600 -94.4300
## 500 meter buffer 0.27440 0.041970 -2395.0000
## 250 meter buffer 0.35630 -0.269500 -202.0000
## cnd(scl(veg_edg)) cnd(scl(wll)) df logLik AICc delta
## 3000 meter buffer -2.92600 1.53200 18 -106.914 253.0 0.00
## 3250 meter buffer -3.27800 1.24400 18 -107.147 253.5 0.47
## 2500 meter buffer -1.75900 1.61500 18 -107.456 254.1 1.09
## 3500 meter buffer -3.68400 1.25900 18 -107.532 254.3 1.24
## 2750 meter buffer -2.21100 1.55700 18 -108.019 255.2 2.21
## 1750 meter buffer -0.85030 1.28600 18 -108.491 256.2 3.15
## 2250 meter buffer -1.39300 1.16900 18 -109.426 258.1 5.02
## 2000 meter buffer -1.30400 1.29900 18 -110.065 259.3 6.30
## 1500 meter buffer -0.88710 0.95770 18 -110.314 259.8 6.80
## 3750 meter buffer -2.87300 1.12000 18 -110.797 260.8 7.77
## 1250 meter buffer -0.16140 0.38550 18 -112.914 265.0 12.00
## 5000 meter buffer 0.05748 0.25730 18 -114.018 267.2 14.21
## 4000 meter buffer -0.44760 0.61300 18 -114.115 267.4 14.40
## 4750 meter buffer 0.38050 0.20930 18 -115.073 269.4 16.32
## 4500 meter buffer 0.54040 0.09117 18 -115.505 270.2 17.18
## 4250 meter buffer 0.26280 0.27810 18 -115.784 270.8 17.74
## 1000 meter buffer -0.10720 0.30250 18 -116.045 271.3 18.26
## 750 meter buffer -0.16640 0.45710 18 -121.221 281.7 28.61
## 500 meter buffer 0.17530 0.38700 18 -127.857 294.9 41.89
## 250 meter buffer 0.02991 0.27320 18 -129.695 298.6 45.56
## weight
## 3000 meter buffer 0.275
## 3250 meter buffer 0.218
## 2500 meter buffer 0.160
## 3500 meter buffer 0.148
## 2750 meter buffer 0.091
## 1750 meter buffer 0.057
## 2250 meter buffer 0.022
## 2000 meter buffer 0.012
## 1500 meter buffer 0.009
## 3750 meter buffer 0.006
## 1250 meter buffer 0.001
## 5000 meter buffer 0.000
## 4000 meter buffer 0.000
## 4750 meter buffer 0.000
## 4500 meter buffer 0.000
## 4250 meter buffer 0.000
## 1000 meter buffer 0.000
## 750 meter buffer 0.000
## 500 meter buffer 0.000
## 250 meter buffer 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)We get a warning that there are some model convergence problems, I expect this is because we don’t have enough data for caribou but I don’t have time to dig into this now so we will investigate more closely for final analysis.
For now let’s examine the top buffer width and see if the model seems reasonable.
For caribou 3000m buffer is top model for now
Model summary 3000m
Let’s take a closer look at the top model summary
summary(caribou_mods$`3000 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(caribou, absent_caribou) ~ scale(harvest) + scale(pipeline) +
## scale(roads) + scale(seismic_lines) + scale(seismic_lines_3D) +
## scale(trails) + scale(transmission_lines) + scale(veg_edges) +
## scale(wells) + scale(osm_industrial) + scale(lc_grassland) +
## scale(lc_coniferous) + scale(lc_broadleaf) + scale(lc_mixed) +
## scale(lc_developed) + scale(lc_shrub) + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 249.8 311.9 -106.9 213.8 214
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.823 0.9072
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -8.32675 1.10886 -7.509 5.95e-14 ***
## scale(harvest) 0.25940 0.59626 0.435 0.66352
## scale(pipeline) -0.87191 0.46951 -1.857 0.06330 .
## scale(roads) 1.11082 1.11248 0.999 0.31803
## scale(seismic_lines) 0.36310 0.27271 1.331 0.18305
## scale(seismic_lines_3D) 0.05807 0.24641 0.236 0.81370
## scale(trails) -0.20199 0.27759 -0.728 0.46683
## scale(transmission_lines) -1.76031 0.96766 -1.819 0.06889 .
## scale(veg_edges) -2.92633 0.97157 -3.012 0.00260 **
## scale(wells) 1.53157 0.51256 2.988 0.00281 **
## scale(osm_industrial) 0.04095 0.66097 0.062 0.95060
## scale(lc_grassland) 1.20931 0.71225 1.698 0.08953 .
## scale(lc_coniferous) 10.37851 4.95897 2.093 0.03636 *
## scale(lc_broadleaf) 2.36607 3.34699 0.707 0.47961
## scale(lc_mixed) 4.58332 2.47197 1.854 0.06372 .
## scale(lc_developed) 3.47022 1.10097 3.152 0.00162 **
## scale(lc_shrub) 5.10412 2.65456 1.923 0.05451 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1There’s nothing that catches my eye immediately as being sus about this particular model so it may not have been one with convergence issues. We will keep it in report for now
Subset Models
Add later if deemed necessary.
Top Model
Coyote
Global models
coyote_mods <- osm_final_df_2021_2022 %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(coyote, absent_coyote) ~
# HFI
scale(harvest) +
scale(pipeline) +
scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(transmission_lines) +
scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
# VEG covariates in numerical order
scale(lc_grassland) +
scale(lc_coniferous) +
scale(lc_broadleaf) +
scale(lc_mixed) +
scale(lc_developed) +
scale(lc_shrub) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(coyote_mods)## Model selection table
## cnd((Int)) dsp((Int)) cnd(scl(hrv)) cnd(scl(lc_brd))
## 2250 meter buffer -1.395 + 0.042500 -0.6109
## 2500 meter buffer -1.395 + 0.042930 -0.6368
## 2000 meter buffer -1.392 + 0.041820 -0.5804
## 2750 meter buffer -1.392 + 0.032190 -0.6176
## 3000 meter buffer -1.392 + 0.027240 -0.6311
## 1750 meter buffer -1.392 + 0.056400 -0.5381
## 3250 meter buffer -1.391 + 0.016470 -0.6451
## 1500 meter buffer -1.392 + 0.064670 -0.5189
## 4750 meter buffer -1.392 + -0.059700 -0.6479
## 3500 meter buffer -1.389 + 0.003136 -0.6677
## 5000 meter buffer -1.392 + -0.065830 -0.6166
## 4500 meter buffer -1.390 + -0.051360 -0.6518
## 1250 meter buffer -1.396 + 0.046620 -0.5118
## 3750 meter buffer -1.388 + 0.007226 -0.6813
## 4250 meter buffer -1.388 + -0.028700 -0.6442
## 4000 meter buffer -1.387 + -0.007103 -0.6526
## 1000 meter buffer -1.401 + 0.039700 -0.4491
## 750 meter buffer -1.407 + 0.071470 -0.4037
## 500 meter buffer -1.405 + 0.026170 -0.2139
## 250 meter buffer -1.401 + -0.070110 -0.2204
## cnd(scl(lc_cnf)) cnd(scl(lc_dvl)) cnd(scl(lc_grs))
## 2250 meter buffer -0.9458 0.15070 -0.23690
## 2500 meter buffer -0.9737 0.11920 -0.23530
## 2000 meter buffer -0.9085 0.17180 -0.23420
## 2750 meter buffer -0.9469 0.09757 -0.20270
## 3000 meter buffer -0.9893 0.09057 -0.19160
## 1750 meter buffer -0.8653 0.19850 -0.20550
## 3250 meter buffer -0.9989 0.09923 -0.19700
## 1500 meter buffer -0.8416 0.20920 -0.16630
## 4750 meter buffer -0.9970 0.13270 -0.30750
## 3500 meter buffer -1.0740 0.11500 -0.23170
## 5000 meter buffer -0.9541 0.17180 -0.30810
## 4500 meter buffer -1.0420 0.14040 -0.31040
## 1250 meter buffer -0.7872 0.23960 -0.15930
## 3750 meter buffer -1.1050 0.12710 -0.24990
## 4250 meter buffer -1.0320 0.12570 -0.28310
## 4000 meter buffer -1.0800 0.10770 -0.25700
## 1000 meter buffer -0.6724 0.29370 -0.13670
## 750 meter buffer -0.5802 0.31140 -0.10340
## 500 meter buffer -0.3256 0.26610 -0.01793
## 250 meter buffer -0.3305 0.26450 -0.04606
## cnd(scl(lc_mxd)) cnd(scl(lc_shr)) cnd(scl(osm_ind))
## 2250 meter buffer -0.52840 -0.48660 0.15630
## 2500 meter buffer -0.52510 -0.50680 0.12900
## 2000 meter buffer -0.51090 -0.45900 0.19800
## 2750 meter buffer -0.49540 -0.48320 0.12130
## 3000 meter buffer -0.50530 -0.48900 0.11380
## 1750 meter buffer -0.50070 -0.41700 0.21220
## 3250 meter buffer -0.49670 -0.48300 0.11090
## 1500 meter buffer -0.50420 -0.39330 0.24070
## 4750 meter buffer -0.40570 -0.43400 0.23000
## 3500 meter buffer -0.51840 -0.50790 0.08270
## 5000 meter buffer -0.38280 -0.41390 0.25370
## 4500 meter buffer -0.43660 -0.44890 0.20870
## 1250 meter buffer -0.48650 -0.37750 0.23710
## 3750 meter buffer -0.53730 -0.50340 0.09756
## 4250 meter buffer -0.45700 -0.44640 0.19250
## 4000 meter buffer -0.51430 -0.47740 0.13990
## 1000 meter buffer -0.41680 -0.28340 0.26170
## 750 meter buffer -0.34620 -0.23400 0.29250
## 500 meter buffer -0.16310 -0.07484 0.26540
## 250 meter buffer -0.08185 -0.05793 0.20090
## cnd(scl(ppl)) cnd(scl(rds)) cnd(scl(ssm_lns_3D))
## 2250 meter buffer -0.19420 0.79880 -0.05081
## 2500 meter buffer -0.20700 0.77430 -0.04427
## 2000 meter buffer -0.12810 0.72600 -0.06077
## 2750 meter buffer -0.23170 0.74130 -0.03743
## 3000 meter buffer -0.20270 0.63160 -0.02955
## 1750 meter buffer -0.08028 0.61160 -0.08205
## 3250 meter buffer -0.15920 0.50920 -0.02971
## 1500 meter buffer -0.04714 0.45540 -0.10910
## 4750 meter buffer 0.02514 0.11170 -0.09480
## 3500 meter buffer -0.11030 0.42650 -0.03691
## 5000 meter buffer 0.03415 0.07007 -0.09585
## 4500 meter buffer 0.05460 0.12340 -0.09086
## 1250 meter buffer -0.00669 0.39200 -0.14390
## 3750 meter buffer -0.08029 0.33360 -0.04971
## 4250 meter buffer 0.02218 0.15910 -0.08018
## 4000 meter buffer -0.02849 0.23380 -0.06312
## 1000 meter buffer 0.05279 0.26340 -0.14670
## 750 meter buffer 0.09082 0.13220 -0.14820
## 500 meter buffer 0.02605 0.01657 -0.11120
## 250 meter buffer 0.11220 -0.14140 -0.03538
## cnd(scl(ssm_lns)) cnd(scl(trl)) cnd(scl(trn_lns))
## 2250 meter buffer 0.3687 0.0278600 0.15570
## 2500 meter buffer 0.3613 0.0129400 0.14920
## 2000 meter buffer 0.3399 0.0342100 0.08978
## 2750 meter buffer 0.3269 0.0012820 0.12700
## 3000 meter buffer 0.3186 0.0017140 0.09048
## 1750 meter buffer 0.3195 0.0472500 0.07428
## 3250 meter buffer 0.3124 0.0038310 0.08569
## 1500 meter buffer 0.3121 0.0418300 0.05657
## 4750 meter buffer 0.3328 -0.0236500 0.08922
## 3500 meter buffer 0.3291 0.0088240 0.07006
## 5000 meter buffer 0.3392 -0.0148300 0.09121
## 4500 meter buffer 0.3408 -0.0149500 0.05294
## 1250 meter buffer 0.3072 0.0224100 -0.01659
## 3750 meter buffer 0.3300 0.0007599 0.07923
## 4250 meter buffer 0.3238 -0.0146400 0.06037
## 4000 meter buffer 0.3275 -0.0057330 0.06099
## 1000 meter buffer 0.2722 0.0020390 -0.04874
## 750 meter buffer 0.2598 -0.0421500 0.02405
## 500 meter buffer 0.1991 -0.0910900 0.08040
## 250 meter buffer 0.1448 -0.0115700 0.02799
## cnd(scl(veg_edg)) cnd(scl(wll)) df logLik AICc delta
## 2250 meter buffer -0.43050 -0.0005123 18 -446.617 932.4 0.00
## 2500 meter buffer -0.38210 0.0412500 18 -447.591 934.4 1.95
## 2000 meter buffer -0.43650 0.0037450 18 -449.597 938.4 5.96
## 2750 meter buffer -0.30720 0.0468400 18 -450.058 939.3 6.88
## 3000 meter buffer -0.20420 0.0654700 18 -451.851 942.9 10.47
## 1750 meter buffer -0.38830 0.0145800 18 -452.179 943.6 11.12
## 3250 meter buffer -0.11390 0.0657700 18 -454.425 948.1 15.62
## 1500 meter buffer -0.29670 0.0411900 18 -455.179 949.6 17.12
## 4750 meter buffer 0.12040 0.1108000 18 -455.299 949.8 17.36
## 3500 meter buffer -0.04928 0.0933100 18 -455.355 949.9 17.47
## 5000 meter buffer 0.10430 0.1126000 18 -455.580 950.4 17.93
## 4500 meter buffer 0.10020 0.1188000 18 -456.109 951.4 18.98
## 1250 meter buffer -0.25020 0.0282600 18 -456.244 951.7 19.25
## 3750 meter buffer -0.01394 0.1228000 18 -457.485 954.2 21.74
## 4250 meter buffer 0.08758 0.1119000 18 -458.373 956.0 23.51
## 4000 meter buffer 0.06694 0.1143000 18 -459.143 957.5 25.05
## 1000 meter buffer -0.24580 0.0890100 18 -460.179 959.6 27.12
## 750 meter buffer -0.26040 0.1760000 18 -463.775 966.8 34.31
## 500 meter buffer -0.24080 0.2046000 18 -478.613 996.4 63.99
## 250 meter buffer -0.16590 0.0354700 18 -493.595 1026.4 93.96
## weight
## 2250 meter buffer 0.680
## 2500 meter buffer 0.257
## 2000 meter buffer 0.035
## 2750 meter buffer 0.022
## 3000 meter buffer 0.004
## 1750 meter buffer 0.003
## 3250 meter buffer 0.000
## 1500 meter buffer 0.000
## 4750 meter buffer 0.000
## 3500 meter buffer 0.000
## 5000 meter buffer 0.000
## 4500 meter buffer 0.000
## 1250 meter buffer 0.000
## 3750 meter buffer 0.000
## 4250 meter buffer 0.000
## 4000 meter buffer 0.000
## 1000 meter buffer 0.000
## 750 meter buffer 0.000
## 500 meter buffer 0.000
## 250 meter buffer 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)for coyote top model appears to be 2250 m
Model summary 2250m
Let’s get the model summary for this model
summary(coyote_mods$`2250 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(coyote, absent_coyote) ~ scale(harvest) + scale(pipeline) +
## scale(roads) + scale(seismic_lines) + scale(seismic_lines_3D) +
## scale(trails) + scale(transmission_lines) + scale(veg_edges) +
## scale(wells) + scale(osm_industrial) + scale(lc_grassland) +
## scale(lc_coniferous) + scale(lc_broadleaf) + scale(lc_mixed) +
## scale(lc_developed) + scale(lc_shrub) + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 929.2 991.3 -446.6 893.2 214
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.04572 0.2138
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.3953463 0.1033031 -13.507 < 2e-16 ***
## scale(harvest) 0.0424989 0.0787950 0.539 0.589638
## scale(pipeline) -0.1942326 0.1112808 -1.745 0.080911 .
## scale(roads) 0.7988319 0.1681427 4.751 2.02e-06 ***
## scale(seismic_lines) 0.3687452 0.0685662 5.378 7.53e-08 ***
## scale(seismic_lines_3D) -0.0508061 0.0633194 -0.802 0.422335
## scale(trails) 0.0278614 0.0639774 0.435 0.663208
## scale(transmission_lines) 0.1557429 0.0947854 1.643 0.100360
## scale(veg_edges) -0.4305253 0.1445842 -2.978 0.002904 **
## scale(wells) -0.0005123 0.1048918 -0.005 0.996103
## scale(osm_industrial) 0.1562622 0.0724134 2.158 0.030934 *
## scale(lc_grassland) -0.2368575 0.0814280 -2.909 0.003628 **
## scale(lc_coniferous) -0.9457625 0.2442679 -3.872 0.000108 ***
## scale(lc_broadleaf) -0.6109090 0.1683292 -3.629 0.000284 ***
## scale(lc_mixed) -0.5283815 0.1511077 -3.497 0.000471 ***
## scale(lc_developed) 0.1506740 0.1159080 1.300 0.193620
## scale(lc_shrub) -0.4865559 0.1392808 -3.493 0.000477 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Everything looks good here
Subset models
Add later if deemed necessary.
Top model
Fisher
Global models
fisher_mods <- osm_final_df_2021_2022 %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(fisher, absent_fisher) ~
# HFI
scale(harvest) +
scale(pipeline) +
scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(transmission_lines) +
scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
# VEG covariates in numerical order
scale(lc_grassland) +
scale(lc_coniferous) +
scale(lc_broadleaf) +
scale(lc_mixed) +
scale(lc_developed) +
scale(lc_shrub) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(fisher_mods)## Model selection table
## cnd((Int)) dsp((Int)) cnd(scl(hrv)) cnd(scl(lc_brd))
## 1000 meter buffer -3.103 + -0.2130 0.7220
## 2250 meter buffer -3.032 + -0.1094 0.4305
## 2500 meter buffer -3.015 + -0.1106 0.4519
## 750 meter buffer -3.073 + -0.2519 0.9443
## 500 meter buffer -3.061 + -0.3083 0.8742
## 2000 meter buffer -3.030 + -0.1302 0.4442
## 2750 meter buffer -3.009 + -0.1313 0.5002
## 3000 meter buffer -3.010 + -0.1380 0.5026
## 1250 meter buffer -3.057 + -0.2260 0.5567
## 3250 meter buffer -3.014 + -0.1605 0.4713
## 4250 meter buffer -3.049 + -0.1686 0.2283
## 4500 meter buffer -3.043 + -0.1821 0.2328
## 1500 meter buffer -3.021 + -0.2114 0.4784
## 4750 meter buffer -3.054 + -0.2156 0.2438
## 1750 meter buffer -3.017 + -0.1744 0.4529
## 4000 meter buffer -3.044 + -0.1668 0.2790
## 250 meter buffer -3.050 + -0.3459 0.0651
## 3500 meter buffer -3.016 + -0.1812 0.4238
## 3750 meter buffer -3.034 + -0.1816 0.3237
## 5000 meter buffer -3.035 + -0.2367 0.2430
## cnd(scl(lc_cnf)) cnd(scl(lc_dvl)) cnd(scl(lc_grs))
## 1000 meter buffer 0.9855 0.4920 0.17270
## 2250 meter buffer 0.6056 0.3171 -0.06029
## 2500 meter buffer 0.6796 0.3220 -0.13240
## 750 meter buffer 1.2770 0.5516 0.25020
## 500 meter buffer 1.1280 0.5176 0.28000
## 2000 meter buffer 0.6137 0.3267 -0.05118
## 2750 meter buffer 0.8144 0.4164 -0.16220
## 3000 meter buffer 0.8642 0.5128 -0.20340
## 1250 meter buffer 0.7391 0.3690 0.10130
## 3250 meter buffer 0.8923 0.5791 -0.21840
## 4250 meter buffer 0.6988 0.8591 -0.14580
## 4500 meter buffer 0.6921 0.9013 -0.11230
## 1500 meter buffer 0.6358 0.2761 0.03435
## 4750 meter buffer 0.7411 0.8550 -0.06847
## 1750 meter buffer 0.6202 0.2558 -0.02444
## 4000 meter buffer 0.7327 0.7886 -0.12320
## 250 meter buffer -0.0501 0.3446 0.03051
## 3500 meter buffer 0.8674 0.6212 -0.18970
## 3750 meter buffer 0.7937 0.7089 -0.14090
## 5000 meter buffer 0.6989 0.8561 -0.05248
## cnd(scl(lc_mxd)) cnd(scl(lc_shr)) cnd(scl(osm_ind))
## 1000 meter buffer 0.43740 -0.1534000 -0.08007
## 2250 meter buffer 0.24660 -0.2819000 -0.17220
## 2500 meter buffer 0.30300 -0.1841000 -0.17230
## 750 meter buffer 0.65500 0.0708200 -0.09660
## 500 meter buffer 0.58170 0.0360900 -0.21500
## 2000 meter buffer 0.25140 -0.2750000 -0.15340
## 2750 meter buffer 0.38860 -0.0505900 -0.11080
## 3000 meter buffer 0.40150 0.0002221 -0.08809
## 1250 meter buffer 0.32890 -0.2144000 -0.13390
## 3250 meter buffer 0.43260 0.0062080 -0.15360
## 4250 meter buffer 0.37530 -0.1945000 -0.38390
## 4500 meter buffer 0.40890 -0.1884000 -0.37390
## 1500 meter buffer 0.27800 -0.1981000 -0.14170
## 4750 meter buffer 0.46060 -0.1798000 -0.47500
## 1750 meter buffer 0.24740 -0.2467000 -0.13110
## 4000 meter buffer 0.35640 -0.1960000 -0.30560
## 250 meter buffer -0.00188 -0.4457000 -0.25220
## 3500 meter buffer 0.42070 -0.0375800 -0.22720
## 3750 meter buffer 0.38160 -0.1261000 -0.29000
## 5000 meter buffer 0.46880 -0.1665000 -0.39550
## cnd(scl(ppl)) cnd(scl(rds)) cnd(scl(ssm_lns_3D))
## 1000 meter buffer -0.4621 0.035280 -0.325200
## 2250 meter buffer -0.2772 0.668800 -0.114300
## 2500 meter buffer -0.3628 0.715700 -0.093810
## 750 meter buffer -0.4509 0.004044 -0.291400
## 500 meter buffer -0.3458 -0.025160 -0.286300
## 2000 meter buffer -0.2613 0.587700 -0.147200
## 2750 meter buffer -0.3731 0.713500 -0.091470
## 3000 meter buffer -0.3673 0.759500 -0.071460
## 1250 meter buffer -0.2649 0.138600 -0.311700
## 3250 meter buffer -0.3980 0.709900 -0.060420
## 4250 meter buffer -0.7518 0.487200 0.001271
## 4500 meter buffer -0.7742 0.379300 0.006289
## 1500 meter buffer -0.2519 0.489900 -0.245300
## 4750 meter buffer -0.7550 0.385200 -0.015980
## 1750 meter buffer -0.1555 0.455900 -0.216000
## 4000 meter buffer -0.7350 0.513400 -0.007683
## 250 meter buffer -0.0961 -0.311300 -0.199800
## 3500 meter buffer -0.4239 0.628500 -0.057820
## 3750 meter buffer -0.5960 0.597500 -0.034360
## 5000 meter buffer -0.6737 0.207600 -0.063910
## cnd(scl(ssm_lns)) cnd(scl(trl)) cnd(scl(trn_lns))
## 1000 meter buffer 0.046110 0.11410 -0.12310
## 2250 meter buffer 0.025740 -0.05722 -0.36660
## 2500 meter buffer -0.023460 -0.05552 -0.26180
## 750 meter buffer 0.026370 0.08005 -0.05458
## 500 meter buffer -0.048660 0.05504 -0.03073
## 2000 meter buffer 0.014350 -0.05207 -0.34080
## 2750 meter buffer -0.077060 -0.07645 -0.22520
## 3000 meter buffer -0.075430 -0.09049 -0.17290
## 1250 meter buffer 0.016590 0.07801 -0.24080
## 3250 meter buffer -0.055620 -0.09053 -0.07487
## 4250 meter buffer 0.121400 -0.05582 0.21500
## 4500 meter buffer 0.125000 -0.04925 0.22600
## 1500 meter buffer 0.004823 0.06190 -0.21700
## 4750 meter buffer 0.137500 -0.01248 0.23230
## 1750 meter buffer -0.005642 0.05866 -0.31110
## 4000 meter buffer 0.111700 -0.03095 0.17610
## 250 meter buffer -0.094330 -0.06221 -0.04710
## 3500 meter buffer -0.012590 -0.03277 -0.01026
## 3750 meter buffer 0.059180 -0.01418 0.11130
## 5000 meter buffer 0.117500 0.01003 0.23000
## cnd(scl(veg_edg)) cnd(scl(wll)) df logLik AICc delta
## 1000 meter buffer 0.08012 0.034220 18 -268.012 575.2 0.00
## 2250 meter buffer -0.13470 -0.117200 18 -268.432 576.1 0.84
## 2500 meter buffer -0.24200 -0.005131 18 -268.638 576.5 1.25
## 750 meter buffer 0.04994 0.071260 18 -268.830 576.9 1.64
## 500 meter buffer 0.06297 0.107700 18 -269.034 577.3 2.04
## 2000 meter buffer -0.13190 -0.075490 18 -269.328 577.9 2.63
## 2750 meter buffer -0.38770 0.047000 18 -269.893 579.0 3.76
## 3000 meter buffer -0.53950 0.052000 18 -270.558 580.3 5.09
## 1250 meter buffer 0.12040 -0.035230 18 -270.684 580.6 5.34
## 3250 meter buffer -0.61820 0.169200 18 -271.005 581.2 5.99
## 4250 meter buffer -0.88250 0.595000 18 -271.430 582.1 6.84
## 4500 meter buffer -0.89610 0.649600 18 -271.748 582.7 7.47
## 1500 meter buffer -0.11120 -0.086720 18 -271.945 583.1 7.86
## 4750 meter buffer -0.85780 0.668100 18 -272.202 583.6 8.38
## 1750 meter buffer -0.02368 -0.084710 18 -272.209 583.6 8.39
## 4000 meter buffer -0.81260 0.515800 18 -272.249 583.7 8.47
## 250 meter buffer -0.11080 -0.104400 18 -272.389 584.0 8.75
## 3500 meter buffer -0.62960 0.231700 18 -272.704 584.6 9.38
## 3750 meter buffer -0.76020 0.389200 18 -272.890 585.0 9.76
## 5000 meter buffer -0.79070 0.643000 18 -273.735 586.7 11.44
## weight
## 1000 meter buffer 0.266
## 2250 meter buffer 0.175
## 2500 meter buffer 0.142
## 750 meter buffer 0.117
## 500 meter buffer 0.096
## 2000 meter buffer 0.071
## 2750 meter buffer 0.040
## 3000 meter buffer 0.021
## 1250 meter buffer 0.018
## 3250 meter buffer 0.013
## 4250 meter buffer 0.009
## 4500 meter buffer 0.006
## 1500 meter buffer 0.005
## 4750 meter buffer 0.004
## 1750 meter buffer 0.004
## 4000 meter buffer 0.004
## 250 meter buffer 0.003
## 3500 meter buffer 0.002
## 3750 meter buffer 0.002
## 5000 meter buffer 0.001
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)For fisher top model is 1000 meter
Model summary 1000m
Let’s print the summary for this model
summary(fisher_mods$`1000 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(fisher, absent_fisher) ~ scale(harvest) + scale(pipeline) +
## scale(roads) + scale(seismic_lines) + scale(seismic_lines_3D) +
## scale(trails) + scale(transmission_lines) + scale(veg_edges) +
## scale(wells) + scale(osm_industrial) + scale(lc_grassland) +
## scale(lc_coniferous) + scale(lc_broadleaf) + scale(lc_mixed) +
## scale(lc_developed) + scale(lc_shrub) + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 572.0 634.1 -268.0 536.0 214
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.2788 0.528
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.10303 0.24991 -12.416 < 2e-16 ***
## scale(harvest) -0.21299 0.11211 -1.900 0.05745 .
## scale(pipeline) -0.46212 0.21441 -2.155 0.03114 *
## scale(roads) 0.03528 0.23091 0.153 0.87858
## scale(seismic_lines) 0.04611 0.09830 0.469 0.63900
## scale(seismic_lines_3D) -0.32519 0.17061 -1.906 0.05665 .
## scale(trails) 0.11406 0.09359 1.219 0.22296
## scale(transmission_lines) -0.12312 0.19015 -0.648 0.51730
## scale(veg_edges) 0.08012 0.21226 0.377 0.70584
## scale(wells) 0.03422 0.12323 0.278 0.78126
## scale(osm_industrial) -0.08007 0.15130 -0.529 0.59666
## scale(lc_grassland) 0.17267 0.13102 1.318 0.18754
## scale(lc_coniferous) 0.98548 0.39150 2.517 0.01183 *
## scale(lc_broadleaf) 0.72202 0.27233 2.651 0.00802 **
## scale(lc_mixed) 0.43743 0.24507 1.785 0.07427 .
## scale(lc_developed) 0.49198 0.17587 2.797 0.00515 **
## scale(lc_shrub) -0.15344 0.26651 -0.576 0.56479
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Everything looks good
Subset models
Add later if deemed necessary.
Top model
Grey wolf
Global models
wolf_mods <- osm_final_df_2021_2022 %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(grey_wolf, absent_grey_wolf) ~
# HFI
scale(harvest) +
scale(pipeline) +
scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(transmission_lines) +
scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
# VEG covariates in numerical order
scale(lc_grassland) +
scale(lc_coniferous) +
scale(lc_broadleaf) +
scale(lc_mixed) +
scale(lc_developed) +
scale(lc_shrub) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(wolf_mods)## Model selection table
## cnd((Int)) dsp((Int)) cnd(scl(hrv)) cnd(scl(lc_brd))
## 500 meter buffer -3.231 + -0.037060 -0.971700
## 3500 meter buffer -3.238 + 0.217800 0.046230
## 3750 meter buffer -3.238 + 0.235300 0.126700
## 4000 meter buffer -3.232 + 0.248500 0.224800
## 3250 meter buffer -3.234 + 0.200700 -0.001385
## 750 meter buffer -3.245 + 0.037480 -0.889900
## 4250 meter buffer -3.222 + 0.219000 0.340700
## 3000 meter buffer -3.230 + 0.188000 -0.074530
## 250 meter buffer -3.268 + -0.027310 -0.672400
## 2000 meter buffer -3.252 + -0.008536 -0.424000
## 1750 meter buffer -3.250 + -0.018120 -0.390000
## 2750 meter buffer -3.229 + 0.156400 -0.142000
## 1000 meter buffer -3.232 + 0.024070 -0.524800
## 1500 meter buffer -3.240 + -0.012710 -0.334600
## 2250 meter buffer -3.241 + 0.028360 -0.380400
## 4500 meter buffer -3.210 + 0.179600 0.387400
## 2500 meter buffer -3.228 + 0.093950 -0.240700
## 4750 meter buffer -3.208 + 0.149400 0.408200
## 5000 meter buffer -3.205 + 0.166600 0.400900
## 1250 meter buffer -3.219 + 0.027900 -0.350400
## cnd(scl(lc_cnf)) cnd(scl(lc_dvl)) cnd(scl(lc_grs))
## 500 meter buffer -0.98650 -0.048790 -0.313100
## 3500 meter buffer 0.39970 0.165800 0.580500
## 3750 meter buffer 0.48230 0.169400 0.607300
## 4000 meter buffer 0.60450 0.150700 0.629700
## 3250 meter buffer 0.34270 0.198500 0.499300
## 750 meter buffer -0.89080 -0.007924 -0.149800
## 4250 meter buffer 0.75150 0.156800 0.608400
## 3000 meter buffer 0.25060 0.214200 0.420900
## 250 meter buffer -0.51490 -0.233900 -0.221000
## 2000 meter buffer -0.10830 0.328200 0.090040
## 1750 meter buffer -0.09863 0.349900 0.050860
## 2750 meter buffer 0.19760 0.256000 0.341900
## 1000 meter buffer -0.40450 0.242500 -0.062530
## 1500 meter buffer -0.06133 0.364000 0.013020
## 2250 meter buffer -0.05733 0.311200 0.138400
## 4500 meter buffer 0.80090 0.083230 0.589100
## 2500 meter buffer 0.09298 0.314300 0.227500
## 4750 meter buffer 0.82190 0.028690 0.589600
## 5000 meter buffer 0.80830 0.062450 0.598900
## 1250 meter buffer -0.14750 0.294400 0.005741
## cnd(scl(lc_mxd)) cnd(scl(lc_shr)) cnd(scl(osm_ind))
## 500 meter buffer -0.4470000 -0.48920 -0.047730
## 3500 meter buffer -0.0122800 0.12360 -0.137300
## 3750 meter buffer -0.0082400 0.18980 -0.102700
## 4000 meter buffer 0.0417800 0.27260 -0.025010
## 3250 meter buffer -0.0100600 0.09504 -0.117900
## 750 meter buffer -0.3254000 -0.51950 0.008087
## 4250 meter buffer 0.1206000 0.37130 0.011010
## 3000 meter buffer -0.0133700 0.07169 -0.091320
## 250 meter buffer -0.1315000 -0.31460 0.083260
## 2000 meter buffer 0.0127700 -0.27740 -0.187200
## 1750 meter buffer 0.0216700 -0.29160 -0.136500
## 2750 meter buffer 0.0069020 0.03526 -0.086450
## 1000 meter buffer -0.1004000 -0.29860 -0.002024
## 1500 meter buffer 0.0376400 -0.25910 -0.097470
## 2250 meter buffer -0.0097320 -0.21460 -0.202000
## 4500 meter buffer 0.1565000 0.38040 -0.052350
## 2500 meter buffer -0.0003944 -0.05267 -0.143300
## 4750 meter buffer 0.1515000 0.33260 -0.201000
## 5000 meter buffer 0.1053000 0.30490 -0.230100
## 1250 meter buffer 0.0059370 -0.18650 -0.019950
## cnd(scl(ppl)) cnd(scl(rds)) cnd(scl(ssm_lns_3D))
## 500 meter buffer 0.34890 -0.38150 -0.9052
## 3500 meter buffer -0.44600 -0.02598 -0.7740
## 3750 meter buffer -0.46090 -0.05365 -0.7688
## 4000 meter buffer -0.52120 -0.12340 -0.7648
## 3250 meter buffer -0.35390 -0.03936 -0.8080
## 750 meter buffer 0.38540 -0.58220 -0.9829
## 4250 meter buffer -0.43160 -0.12370 -0.7782
## 3000 meter buffer -0.31250 -0.08431 -0.8640
## 250 meter buffer 0.22500 -0.18480 -1.2650
## 2000 meter buffer 0.19270 -0.33400 -0.9736
## 1750 meter buffer 0.29720 -0.46840 -0.9661
## 2750 meter buffer -0.24670 -0.10280 -0.9034
## 1000 meter buffer 0.37390 -0.67980 -0.9062
## 1500 meter buffer 0.34370 -0.62370 -0.9327
## 2250 meter buffer 0.03818 -0.14240 -0.9378
## 4500 meter buffer -0.35220 0.01628 -0.7679
## 2500 meter buffer -0.11820 -0.10630 -0.9231
## 4750 meter buffer -0.40130 0.25730 -0.6983
## 5000 meter buffer -0.52980 0.31470 -0.6436
## 1250 meter buffer 0.33120 -0.63520 -0.9027
## cnd(scl(ssm_lns)) cnd(scl(trl)) cnd(scl(trn_lns))
## 500 meter buffer 0.125000 0.08796 -0.19730
## 3500 meter buffer -0.089180 0.06981 0.26460
## 3750 meter buffer -0.100500 0.06518 0.26380
## 4000 meter buffer -0.103300 0.03829 0.29540
## 3250 meter buffer -0.063300 0.07017 0.22960
## 750 meter buffer 0.157100 -0.01028 -0.12220
## 4250 meter buffer -0.113600 0.05983 0.20550
## 3000 meter buffer -0.029190 0.05093 0.24090
## 250 meter buffer 0.036700 0.20850 -0.25300
## 2000 meter buffer 0.021150 0.21190 -0.05804
## 1750 meter buffer -0.018600 0.21330 -0.11380
## 2750 meter buffer -0.003554 0.05788 0.21810
## 1000 meter buffer 0.086680 0.07459 -0.06863
## 1500 meter buffer -0.002519 0.20930 -0.13230
## 2250 meter buffer 0.024590 0.16410 0.02337
## 4500 meter buffer -0.130500 0.08058 0.08265
## 2500 meter buffer 0.015410 0.10940 0.14540
## 4750 meter buffer -0.131200 0.07740 0.00401
## 5000 meter buffer -0.114900 0.05334 0.05140
## 1250 meter buffer 0.024350 0.13480 -0.09049
## cnd(scl(veg_edg)) cnd(scl(wll)) df logLik AICc delta
## 500 meter buffer -0.145700 -0.28210 18 -236.653 512.5 0.00
## 3500 meter buffer -0.188000 -0.22190 18 -237.226 513.7 1.15
## 3750 meter buffer -0.176000 -0.24700 18 -237.463 514.1 1.62
## 4000 meter buffer -0.176000 -0.16900 18 -237.738 514.7 2.17
## 3250 meter buffer -0.236700 -0.21760 18 -238.268 515.7 3.23
## 750 meter buffer -0.092290 -0.02706 18 -238.652 516.5 4.00
## 4250 meter buffer -0.156000 -0.21800 18 -239.238 517.7 5.17
## 3000 meter buffer -0.284700 -0.14220 18 -239.529 518.3 5.75
## 250 meter buffer -0.082900 -0.06612 18 -239.769 518.7 6.23
## 2000 meter buffer -0.156100 -0.13170 18 -239.864 518.9 6.42
## 1750 meter buffer -0.093230 -0.12370 18 -239.948 519.1 6.59
## 2750 meter buffer -0.331100 -0.09790 18 -240.224 519.7 7.14
## 1000 meter buffer -0.124800 -0.06659 18 -240.303 519.8 7.30
## 1500 meter buffer -0.032900 -0.06583 18 -240.428 520.1 7.55
## 2250 meter buffer -0.240100 -0.19080 18 -240.681 520.6 8.06
## 4500 meter buffer -0.085510 -0.31020 18 -240.762 520.7 8.22
## 2500 meter buffer -0.323900 -0.14500 18 -241.169 521.5 9.03
## 4750 meter buffer 0.005409 -0.44860 18 -241.369 521.9 9.43
## 5000 meter buffer -0.002138 -0.45350 18 -241.521 522.3 9.74
## 1250 meter buffer -0.102500 -0.03946 18 -241.850 522.9 10.39
## weight
## 500 meter buffer 0.325
## 3500 meter buffer 0.183
## 3750 meter buffer 0.145
## 4000 meter buffer 0.110
## 3250 meter buffer 0.065
## 750 meter buffer 0.044
## 4250 meter buffer 0.024
## 3000 meter buffer 0.018
## 250 meter buffer 0.014
## 2000 meter buffer 0.013
## 1750 meter buffer 0.012
## 2750 meter buffer 0.009
## 1000 meter buffer 0.008
## 1500 meter buffer 0.007
## 2250 meter buffer 0.006
## 4500 meter buffer 0.005
## 2500 meter buffer 0.004
## 4750 meter buffer 0.003
## 5000 meter buffer 0.002
## 1250 meter buffer 0.002
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)For grey wolf top model is 500 m buffer
Model summary 500m
Let’s get the model summary for this model
summary(wolf_mods$`500 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(grey_wolf, absent_grey_wolf) ~ scale(harvest) + scale(pipeline) +
## scale(roads) + scale(seismic_lines) + scale(seismic_lines_3D) +
## scale(trails) + scale(transmission_lines) + scale(veg_edges) +
## scale(wells) + scale(osm_industrial) + scale(lc_grassland) +
## scale(lc_coniferous) + scale(lc_broadleaf) + scale(lc_mixed) +
## scale(lc_developed) + scale(lc_shrub) + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 509.3 571.3 -236.7 473.3 214
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 5.924e-10 2.434e-05
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.23077 0.13696 -23.589 < 2e-16 ***
## scale(harvest) -0.03706 0.09169 -0.404 0.68611
## scale(pipeline) 0.34887 0.15217 2.293 0.02187 *
## scale(roads) -0.38147 0.19999 -1.907 0.05646 .
## scale(seismic_lines) 0.12504 0.08754 1.428 0.15320
## scale(seismic_lines_3D) -0.90518 0.27643 -3.275 0.00106 **
## scale(trails) 0.08796 0.08212 1.071 0.28410
## scale(transmission_lines) -0.19728 0.14199 -1.389 0.16470
## scale(veg_edges) -0.14566 0.14749 -0.988 0.32336
## scale(wells) -0.28215 0.15563 -1.813 0.06984 .
## scale(osm_industrial) -0.04773 0.10158 -0.470 0.63844
## scale(lc_grassland) -0.31308 0.15267 -2.051 0.04030 *
## scale(lc_coniferous) -0.98649 0.41080 -2.401 0.01633 *
## scale(lc_broadleaf) -0.97169 0.29859 -3.254 0.00114 **
## scale(lc_mixed) -0.44697 0.24027 -1.860 0.06284 .
## scale(lc_developed) -0.04879 0.17199 -0.284 0.77665
## scale(lc_shrub) -0.48916 0.23223 -2.106 0.03517 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1interesting that seismic lines weren’t significant but 3d seismic were and have a negative estimate
Subset models
Add later if deemed necessary.
Top model
Lynx
Global models
lynx_mods <- osm_final_df_2021_2022 %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(lynx, absent_lynx) ~
# HFI
scale(harvest) +
scale(pipeline) +
scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(transmission_lines) +
scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
# VEG covariates in numerical order
scale(lc_grassland) +
scale(lc_coniferous) +
scale(lc_broadleaf) +
scale(lc_mixed) +
scale(lc_developed) +
scale(lc_shrub) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(lynx_mods)## Model selection table
## cnd((Int)) dsp((Int)) cnd(scl(hrv)) cnd(scl(lc_brd))
## 1250 meter buffer -1.998 + -0.10700 0.951500
## 1000 meter buffer -1.999 + -0.10950 1.200000
## 1500 meter buffer -1.983 + -0.08890 0.510000
## 500 meter buffer -1.979 + -0.20620 1.094000
## 750 meter buffer -1.982 + -0.14150 1.202000
## 1750 meter buffer -1.972 + -0.04953 0.395600
## 250 meter buffer -1.957 + -0.31350 0.514700
## 2000 meter buffer -1.964 + -0.01751 0.297200
## 2250 meter buffer -1.951 + 0.03254 0.173700
## 2500 meter buffer -1.940 + 0.08284 0.065000
## 2750 meter buffer -1.934 + 0.12560 -0.002596
## 5000 meter buffer -1.920 + 0.15490 -0.271800
## 3000 meter buffer -1.932 + 0.14720 -0.008921
## 3250 meter buffer -1.931 + 0.15490 -0.009491
## 4750 meter buffer -1.919 + 0.16900 -0.306100
## 3500 meter buffer -1.926 + 0.15950 -0.064560
## 3750 meter buffer -1.925 + 0.16140 -0.139600
## 4250 meter buffer -1.923 + 0.17840 -0.249800
## 4000 meter buffer -1.923 + 0.16860 -0.220800
## 4500 meter buffer -1.920 + 0.17420 -0.271800
## cnd(scl(lc_cnf)) cnd(scl(lc_dvl)) cnd(scl(lc_grs))
## 1250 meter buffer 2.2940 0.5731 0.52720
## 1000 meter buffer 2.5530 0.6433 0.59470
## 1500 meter buffer 1.6460 0.4934 0.36640
## 500 meter buffer 2.2360 0.5143 0.72110
## 750 meter buffer 2.4650 0.6015 0.62100
## 1750 meter buffer 1.4670 0.4982 0.26500
## 250 meter buffer 1.3370 0.2788 0.44470
## 2000 meter buffer 1.3040 0.4947 0.23210
## 2250 meter buffer 1.0990 0.4593 0.18130
## 2500 meter buffer 0.8984 0.4509 0.16350
## 2750 meter buffer 0.7380 0.4410 0.18500
## 5000 meter buffer 0.3504 0.3159 -0.02850
## 3000 meter buffer 0.6937 0.4508 0.18720
## 3250 meter buffer 0.7028 0.4774 0.19180
## 4750 meter buffer 0.3355 0.3516 -0.03058
## 3500 meter buffer 0.6110 0.4454 0.15500
## 3750 meter buffer 0.5303 0.4425 0.11400
## 4250 meter buffer 0.4247 0.4277 0.06568
## 4000 meter buffer 0.4381 0.4143 0.07779
## 4500 meter buffer 0.3901 0.4032 0.01749
## cnd(scl(lc_mxd)) cnd(scl(lc_shr)) cnd(scl(osm_ind))
## 1250 meter buffer 1.26600 1.2370 0.18780
## 1000 meter buffer 1.41900 1.3690 0.22610
## 1500 meter buffer 0.89640 0.8896 0.12510
## 500 meter buffer 1.14200 1.1440 0.22890
## 750 meter buffer 1.35700 1.2830 0.25180
## 1750 meter buffer 0.77230 0.7947 0.10610
## 250 meter buffer 0.73140 0.7349 0.16530
## 2000 meter buffer 0.67270 0.6892 0.08232
## 2250 meter buffer 0.53040 0.5593 0.06887
## 2500 meter buffer 0.39370 0.4528 0.07333
## 2750 meter buffer 0.26820 0.3755 0.05677
## 5000 meter buffer 0.09991 0.1316 -0.05720
## 3000 meter buffer 0.21590 0.3483 0.05043
## 3250 meter buffer 0.22290 0.3610 0.06459
## 4750 meter buffer 0.09305 0.1285 -0.02188
## 3500 meter buffer 0.17910 0.3089 0.03058
## 3750 meter buffer 0.14790 0.2674 0.02198
## 4250 meter buffer 0.12320 0.2027 0.03087
## 4000 meter buffer 0.11450 0.2126 0.01585
## 4500 meter buffer 0.11010 0.1744 0.02351
## cnd(scl(ppl)) cnd(scl(rds)) cnd(scl(ssm_lns_3D))
## 1250 meter buffer 0.011650 0.18440 0.08558
## 1000 meter buffer 0.044020 0.04073 0.08290
## 1500 meter buffer 0.018580 0.34000 0.10820
## 500 meter buffer -0.008030 0.03130 0.06549
## 750 meter buffer -0.003399 0.01784 0.06542
## 1750 meter buffer 0.014920 0.46950 0.12480
## 250 meter buffer 0.016210 0.02852 0.04631
## 2000 meter buffer -0.014960 0.53310 0.13510
## 2250 meter buffer -0.081600 0.56500 0.13330
## 2500 meter buffer -0.107300 0.42190 0.12300
## 2750 meter buffer -0.077360 0.34500 0.12380
## 5000 meter buffer 0.082550 0.39330 0.14820
## 3000 meter buffer -0.061900 0.29280 0.12390
## 3250 meter buffer -0.044800 0.21130 0.11950
## 4750 meter buffer 0.022940 0.30420 0.11610
## 3500 meter buffer -0.061890 0.23630 0.10620
## 3750 meter buffer -0.039720 0.20790 0.09828
## 4250 meter buffer -0.028600 0.15290 0.09666
## 4000 meter buffer -0.033380 0.18580 0.09580
## 4500 meter buffer 0.008188 0.19140 0.10340
## cnd(scl(ssm_lns)) cnd(scl(trl)) cnd(scl(trn_lns))
## 1250 meter buffer -0.003312 0.11570 -0.083150
## 1000 meter buffer -0.031170 0.10750 -0.047370
## 1500 meter buffer -0.007120 0.12780 -0.075910
## 500 meter buffer -0.086320 -0.02598 -0.078670
## 750 meter buffer -0.044540 0.02683 -0.004659
## 1750 meter buffer -0.028230 0.11330 -0.047020
## 250 meter buffer -0.153400 0.03605 -0.028840
## 2000 meter buffer -0.026320 0.11000 -0.023570
## 2250 meter buffer -0.055640 0.09932 0.039340
## 2500 meter buffer -0.072270 0.08368 0.087040
## 2750 meter buffer -0.075140 0.08633 0.053090
## 5000 meter buffer -0.123300 0.13080 -0.097910
## 3000 meter buffer -0.089490 0.09532 0.021930
## 3250 meter buffer -0.109400 0.09882 0.012100
## 4750 meter buffer -0.115700 0.12240 -0.025950
## 3500 meter buffer -0.123300 0.10480 0.035380
## 3750 meter buffer -0.126600 0.10650 0.023980
## 4250 meter buffer -0.123400 0.10910 0.026350
## 4000 meter buffer -0.124500 0.10590 0.018090
## 4500 meter buffer -0.110100 0.11820 -0.009629
## cnd(scl(veg_edg)) cnd(scl(wll)) df logLik AICc delta
## 1250 meter buffer -0.3415 -0.2457 18 -414.250 867.7 0.00
## 1000 meter buffer -0.3337 -0.1743 18 -414.822 868.9 1.14
## 1500 meter buffer -0.4351 -0.2975 18 -416.528 872.3 4.56
## 500 meter buffer -0.3271 -0.1569 18 -416.655 872.5 4.81
## 750 meter buffer -0.3294 -0.1261 18 -418.644 876.5 8.79
## 1750 meter buffer -0.5412 -0.3438 18 -419.271 877.8 10.04
## 250 meter buffer -0.1747 -0.1823 18 -420.007 879.2 11.51
## 2000 meter buffer -0.5642 -0.4024 18 -421.235 881.7 13.97
## 2250 meter buffer -0.5536 -0.4200 18 -424.464 888.1 20.43
## 2500 meter buffer -0.4965 -0.3664 18 -427.631 894.5 26.76
## 2750 meter buffer -0.4432 -0.3658 18 -429.440 898.1 30.38
## 5000 meter buffer -0.2836 -0.3014 18 -429.999 899.2 31.50
## 3000 meter buffer -0.4081 -0.3515 18 -430.196 899.6 31.89
## 3250 meter buffer -0.3866 -0.3170 18 -430.805 900.8 33.11
## 4750 meter buffer -0.3000 -0.2467 18 -431.395 902.0 34.29
## 3500 meter buffer -0.3665 -0.2986 18 -431.591 902.4 34.68
## 3750 meter buffer -0.3423 -0.2757 18 -432.198 903.6 35.90
## 4250 meter buffer -0.3173 -0.2238 18 -432.979 905.2 37.46
## 4000 meter buffer -0.3108 -0.2373 18 -433.147 905.5 37.79
## 4500 meter buffer -0.3208 -0.2096 18 -433.179 905.6 37.86
## weight
## 1250 meter buffer 0.562
## 1000 meter buffer 0.317
## 1500 meter buffer 0.058
## 500 meter buffer 0.051
## 750 meter buffer 0.007
## 1750 meter buffer 0.004
## 250 meter buffer 0.002
## 2000 meter buffer 0.001
## 2250 meter buffer 0.000
## 2500 meter buffer 0.000
## 2750 meter buffer 0.000
## 5000 meter buffer 0.000
## 3000 meter buffer 0.000
## 3250 meter buffer 0.000
## 4750 meter buffer 0.000
## 3500 meter buffer 0.000
## 3750 meter buffer 0.000
## 4250 meter buffer 0.000
## 4000 meter buffer 0.000
## 4500 meter buffer 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)For lynx the top model is the 1250 m buffer
Model summary 1250m
Let’s get the model summary
summary(lynx_mods$`1250 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(lynx, absent_lynx) ~ scale(harvest) + scale(pipeline) +
## scale(roads) + scale(seismic_lines) + scale(seismic_lines_3D) +
## scale(trails) + scale(transmission_lines) + scale(veg_edges) +
## scale(wells) + scale(osm_industrial) + scale(lc_grassland) +
## scale(lc_coniferous) + scale(lc_broadleaf) + scale(lc_mixed) +
## scale(lc_developed) + scale(lc_shrub) + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 864.5 926.5 -414.2 828.5 214
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.02967 0.1722
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.997708 0.097494 -20.491 < 2e-16 ***
## scale(harvest) -0.106953 0.081260 -1.316 0.18812
## scale(pipeline) 0.011650 0.114082 0.102 0.91866
## scale(roads) 0.184363 0.187036 0.986 0.32428
## scale(seismic_lines) -0.003312 0.067892 -0.049 0.96110
## scale(seismic_lines_3D) 0.085578 0.061810 1.385 0.16620
## scale(trails) 0.115665 0.055589 2.081 0.03746 *
## scale(transmission_lines) -0.083153 0.106651 -0.780 0.43558
## scale(veg_edges) -0.341523 0.134226 -2.544 0.01095 *
## scale(wells) -0.245720 0.098236 -2.501 0.01237 *
## scale(osm_industrial) 0.187831 0.072422 2.594 0.00950 **
## scale(lc_grassland) 0.527181 0.127159 4.146 3.39e-05 ***
## scale(lc_coniferous) 2.294419 0.554694 4.136 3.53e-05 ***
## scale(lc_broadleaf) 0.951507 0.358493 2.654 0.00795 **
## scale(lc_mixed) 1.265559 0.314719 4.021 5.79e-05 ***
## scale(lc_developed) 0.573095 0.144770 3.959 7.54e-05 ***
## scale(lc_shrub) 1.237013 0.306204 4.040 5.35e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Everything looks good so far
Subset models
Add later if deemed necessary.
Top model
Moose
Global model
moose_mods <- osm_final_df_2021_2022 %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(moose, absent_moose) ~
# HFI
scale(harvest) +
scale(pipeline) +
scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(transmission_lines) +
scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
# VEG covariates in numerical order
scale(lc_grassland) +
scale(lc_coniferous) +
scale(lc_broadleaf) +
scale(lc_mixed) +
scale(lc_developed) +
scale(lc_shrub) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(moose_mods)## Model selection table
## cnd((Int)) dsp((Int)) cnd(scl(hrv)) cnd(scl(lc_brd))
## 250 meter buffer -1.878 + -0.002032 -0.04093
## 500 meter buffer -1.881 + 0.007602 -0.15570
## 750 meter buffer -1.879 + 0.018560 0.04249
## 5000 meter buffer -1.882 + 0.005041 0.97770
## 1000 meter buffer -1.871 + 0.010350 0.00255
## 4750 meter buffer -1.877 + 0.002859 0.93390
## 3750 meter buffer -1.869 + 0.023660 0.60380
## 4000 meter buffer -1.869 + 0.022960 0.69640
## 4500 meter buffer -1.869 + 0.002659 0.85200
## 3500 meter buffer -1.865 + 0.033830 0.54850
## 4250 meter buffer -1.867 + 0.010490 0.77670
## 3250 meter buffer -1.862 + 0.046550 0.47340
## 3000 meter buffer -1.863 + 0.057610 0.40840
## 2750 meter buffer -1.860 + 0.064590 0.32600
## 2500 meter buffer -1.856 + 0.070920 0.28420
## 1250 meter buffer -1.857 + 0.039370 -0.01379
## 2250 meter buffer -1.849 + 0.067100 0.21110
## 1500 meter buffer -1.851 + 0.042190 -0.01124
## 1750 meter buffer -1.845 + 0.041230 0.03104
## 2000 meter buffer -1.843 + 0.032260 0.13520
## cnd(scl(lc_cnf)) cnd(scl(lc_dvl)) cnd(scl(lc_grs))
## 250 meter buffer -0.58030 -0.33560 -0.1402000
## 500 meter buffer -0.74630 -0.18660 -0.1253000
## 750 meter buffer -0.47410 -0.08463 -0.0381800
## 5000 meter buffer 0.79260 0.54480 0.0062190
## 1000 meter buffer -0.50940 -0.08745 -0.0597700
## 4750 meter buffer 0.72150 0.51900 0.0475100
## 3750 meter buffer 0.19970 0.24010 -0.0047560
## 4000 meter buffer 0.32480 0.35190 -0.0001991
## 4500 meter buffer 0.57510 0.41390 0.0439300
## 3500 meter buffer 0.12590 0.18550 0.0138500
## 4250 meter buffer 0.45770 0.37710 0.0269800
## 3250 meter buffer 0.03208 0.09211 0.0338300
## 3000 meter buffer -0.05974 -0.01656 0.0534800
## 2750 meter buffer -0.18380 -0.14370 0.0713600
## 2500 meter buffer -0.23100 -0.13840 0.0705900
## 1250 meter buffer -0.52390 -0.07189 -0.0273000
## 2250 meter buffer -0.31020 -0.10800 0.0484400
## 1500 meter buffer -0.51040 -0.04036 -0.0108500
## 1750 meter buffer -0.47420 -0.09248 0.0164000
## 2000 meter buffer -0.36670 -0.10310 0.0273400
## cnd(scl(lc_mxd)) cnd(scl(lc_shr)) cnd(scl(osm_ind))
## 250 meter buffer -0.27790 -0.088590 -0.1908
## 500 meter buffer -0.38860 -0.236700 -0.3299
## 750 meter buffer -0.17030 -0.134300 -0.3327
## 5000 meter buffer 0.21320 0.604400 -0.2861
## 1000 meter buffer -0.20600 -0.126800 -0.2908
## 4750 meter buffer 0.20820 0.563100 -0.2792
## 3750 meter buffer 0.08199 0.286600 -0.4126
## 4000 meter buffer 0.11870 0.365200 -0.3352
## 4500 meter buffer 0.17090 0.489900 -0.3017
## 3500 meter buffer 0.04727 0.232000 -0.3975
## 4250 meter buffer 0.15780 0.431400 -0.3017
## 3250 meter buffer -0.01014 0.157900 -0.4158
## 3000 meter buffer -0.07013 0.099760 -0.4314
## 2750 meter buffer -0.15070 0.029640 -0.4406
## 2500 meter buffer -0.17710 0.009509 -0.3517
## 1250 meter buffer -0.24480 -0.125200 -0.2146
## 2250 meter buffer -0.19270 -0.026350 -0.2546
## 1500 meter buffer -0.26390 -0.135500 -0.2362
## 1750 meter buffer -0.26020 -0.125100 -0.2128
## 2000 meter buffer -0.19500 -0.076940 -0.2405
## cnd(scl(ppl)) cnd(scl(rds)) cnd(scl(ssm_lns_3D))
## 250 meter buffer -0.006781 -0.19920 0.18590
## 500 meter buffer 0.102800 -0.22250 0.15950
## 750 meter buffer 0.174600 -0.22750 0.12520
## 5000 meter buffer -0.127700 0.33710 0.05034
## 1000 meter buffer 0.232800 -0.25860 0.08750
## 4750 meter buffer -0.132200 0.31090 0.06682
## 3750 meter buffer -0.010510 0.38230 0.03537
## 4000 meter buffer -0.003876 0.27980 0.04015
## 4500 meter buffer -0.083720 0.30210 0.05842
## 3500 meter buffer -0.027980 0.37520 0.03467
## 4250 meter buffer -0.066350 0.25880 0.04796
## 3250 meter buffer -0.065050 0.43670 0.03313
## 3000 meter buffer -0.101600 0.49180 0.02630
## 2750 meter buffer -0.131500 0.49970 0.01676
## 2500 meter buffer -0.160600 0.34010 0.01979
## 1250 meter buffer 0.186900 -0.28900 0.07527
## 2250 meter buffer -0.099920 0.13580 0.03606
## 1500 meter buffer 0.132800 -0.25840 0.07116
## 1750 meter buffer 0.040180 -0.11020 0.05845
## 2000 meter buffer 0.006445 0.03398 0.04289
## cnd(scl(ssm_lns)) cnd(scl(trl)) cnd(scl(trn_lns))
## 250 meter buffer 0.041250 0.052820 9.152e-02
## 500 meter buffer 0.028140 0.045090 3.699e-02
## 750 meter buffer -0.025640 0.035850 -8.078e-03
## 5000 meter buffer -0.078360 -0.015410 3.037e-01
## 1000 meter buffer -0.077310 0.045370 -5.423e-02
## 4750 meter buffer -0.081570 -0.008001 2.681e-01
## 3750 meter buffer -0.100500 0.037600 2.074e-01
## 4000 meter buffer -0.107100 0.025630 2.002e-01
## 4500 meter buffer -0.091260 0.010430 2.228e-01
## 3500 meter buffer -0.097610 0.042530 2.104e-01
## 4250 meter buffer -0.104300 0.013880 2.196e-01
## 3250 meter buffer -0.074480 0.044980 2.194e-01
## 3000 meter buffer -0.057350 0.047050 2.386e-01
## 2750 meter buffer -0.022730 0.051450 2.552e-01
## 2500 meter buffer -0.009657 0.044060 2.791e-01
## 1250 meter buffer -0.036630 0.047520 -2.164e-02
## 2250 meter buffer -0.013640 0.030010 2.044e-01
## 1500 meter buffer -0.042620 0.061730 6.225e-05
## 1750 meter buffer -0.044150 0.043640 7.419e-02
## 2000 meter buffer -0.019850 0.049330 8.495e-02
## cnd(scl(veg_edg)) cnd(scl(wll)) df logLik AICc delta
## 250 meter buffer 0.110900 -0.05671 18 -408.795 856.8 0.00
## 500 meter buffer 0.011790 -0.07600 18 -408.932 857.1 0.27
## 750 meter buffer -0.010410 -0.09323 18 -411.103 861.4 4.62
## 5000 meter buffer -0.626700 -0.22700 18 -413.830 866.9 10.07
## 1000 meter buffer 0.017430 -0.09916 18 -414.280 867.8 10.97
## 4750 meter buffer -0.575100 -0.26440 18 -416.094 871.4 14.60
## 3750 meter buffer -0.390600 -0.27620 18 -417.695 874.6 17.80
## 4000 meter buffer -0.460500 -0.24140 18 -417.747 874.7 17.90
## 4500 meter buffer -0.498600 -0.24690 18 -417.903 875.0 18.22
## 3500 meter buffer -0.343500 -0.30270 18 -418.307 875.8 19.02
## 4250 meter buffer -0.463700 -0.21590 18 -418.360 875.9 19.13
## 3250 meter buffer -0.282800 -0.33550 18 -419.112 877.4 20.63
## 3000 meter buffer -0.212500 -0.36570 18 -419.159 877.5 20.73
## 2750 meter buffer -0.115200 -0.34760 18 -419.237 877.7 20.88
## 2500 meter buffer -0.056660 -0.32340 18 -419.865 878.9 22.14
## 1250 meter buffer 0.042460 -0.06342 18 -419.892 879.0 22.19
## 2250 meter buffer 0.006556 -0.28980 18 -422.400 884.0 27.21
## 1500 meter buffer 0.085920 -0.11490 18 -422.726 884.7 27.86
## 1750 meter buffer 0.060070 -0.20410 18 -424.194 887.6 30.80
## 2000 meter buffer 0.082470 -0.29880 18 -424.559 888.3 31.53
## weight
## 250 meter buffer 0.504
## 500 meter buffer 0.440
## 750 meter buffer 0.050
## 5000 meter buffer 0.003
## 1000 meter buffer 0.002
## 4750 meter buffer 0.000
## 3750 meter buffer 0.000
## 4000 meter buffer 0.000
## 4500 meter buffer 0.000
## 3500 meter buffer 0.000
## 4250 meter buffer 0.000
## 3250 meter buffer 0.000
## 3000 meter buffer 0.000
## 2750 meter buffer 0.000
## 2500 meter buffer 0.000
## 1250 meter buffer 0.000
## 2250 meter buffer 0.000
## 1500 meter buffer 0.000
## 1750 meter buffer 0.000
## 2000 meter buffer 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)For moose the top model is the 250 m buffer
Model summary 250m
Let’s get the model summary
summary(moose_mods$`250 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(moose, absent_moose) ~ scale(harvest) + scale(pipeline) +
## scale(roads) + scale(seismic_lines) + scale(seismic_lines_3D) +
## scale(trails) + scale(transmission_lines) + scale(veg_edges) +
## scale(wells) + scale(osm_industrial) + scale(lc_grassland) +
## scale(lc_coniferous) + scale(lc_broadleaf) + scale(lc_mixed) +
## scale(lc_developed) + scale(lc_shrub) + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 853.6 915.6 -408.8 817.6 214
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.3108 0.5575
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.877557 0.236425 -7.941 2e-15 ***
## scale(harvest) -0.002032 0.062622 -0.032 0.974117
## scale(pipeline) -0.006781 0.082485 -0.082 0.934477
## scale(roads) -0.199217 0.116670 -1.708 0.087723 .
## scale(seismic_lines) 0.041253 0.063722 0.647 0.517373
## scale(seismic_lines_3D) 0.185895 0.055703 3.337 0.000846 ***
## scale(trails) 0.052822 0.053881 0.980 0.326916
## scale(transmission_lines) 0.091525 0.076611 1.195 0.232217
## scale(veg_edges) 0.110934 0.099668 1.113 0.265694
## scale(wells) -0.056712 0.070758 -0.801 0.422849
## scale(osm_industrial) -0.190844 0.072207 -2.643 0.008217 **
## scale(lc_grassland) -0.140190 0.106892 -1.312 0.189684
## scale(lc_coniferous) -0.580280 0.272816 -2.127 0.033420 *
## scale(lc_broadleaf) -0.040927 0.190428 -0.215 0.829830
## scale(lc_mixed) -0.277906 0.169142 -1.643 0.100376
## scale(lc_developed) -0.335609 0.120540 -2.784 0.005366 **
## scale(lc_shrub) -0.088587 0.152663 -0.580 0.561727
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Looks good for now
Subset models
Add later if deemed necessary
Top model
Red fox
Global models
fox_mods <- osm_final_df_2021_2022 %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(red_fox, absent_red_fox) ~
# HFI
scale(harvest) +
scale(pipeline) +
scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(transmission_lines) +
scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
# VEG covariates in numerical order
scale(lc_grassland) +
scale(lc_coniferous) +
scale(lc_broadleaf) +
scale(lc_mixed) +
scale(lc_developed) +
scale(lc_shrub) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(fox_mods)## Model selection table
## cnd((Int)) dsp((Int)) cnd(scl(hrv)) cnd(scl(lc_brd))
## 4750 meter buffer -5.482 + 0.33900 -0.59670
## 5000 meter buffer -5.317 + 0.30480 -0.51060
## 4500 meter buffer -5.305 + 0.34250 -0.83470
## 1000 meter buffer -4.590 + -0.23040 -0.27900
## 1250 meter buffer -4.625 + -0.25760 -0.29670
## 1500 meter buffer -4.798 + -0.18940 -0.15750
## 1750 meter buffer -4.856 + -0.11410 -0.04801
## 2500 meter buffer -4.819 + 0.13900 0.10620
## 2250 meter buffer -4.687 + -0.02548 0.11060
## 4250 meter buffer -4.932 + 0.31360 -1.02500
## 2000 meter buffer -4.720 + -0.03125 0.07487
## 750 meter buffer -4.585 + -0.23310 -0.10160
## 4000 meter buffer -4.784 + 0.26090 -0.82660
## 2750 meter buffer -4.660 + 0.15870 -0.02933
## 3750 meter buffer -4.717 + 0.15350 -0.65610
## 500 meter buffer -4.471 + -0.14050 0.62930
## 3000 meter buffer -4.645 + 0.18320 -0.24890
## 250 meter buffer -4.526 + -0.12290 -0.30710
## 3500 meter buffer -4.575 + 0.12620 -0.57170
## 3250 meter buffer -4.501 + 0.22740 -0.63270
## cnd(scl(lc_cnf)) cnd(scl(lc_dvl)) cnd(scl(lc_grs))
## 4750 meter buffer -1.7340 -1.94600 0.45870
## 5000 meter buffer -1.6740 -2.14800 0.39790
## 4500 meter buffer -1.8170 -1.53100 0.42530
## 1000 meter buffer -0.9658 -0.19720 0.31940
## 1250 meter buffer -1.0170 -0.30950 0.35830
## 1500 meter buffer -0.8697 -0.32920 0.52600
## 1750 meter buffer -0.7501 -0.46890 0.64170
## 2500 meter buffer -0.6056 -0.70630 0.87200
## 2250 meter buffer -0.5304 -0.33220 0.87670
## 4250 meter buffer -1.9290 -1.32700 0.28900
## 2000 meter buffer -0.5677 -0.40830 0.72750
## 750 meter buffer -0.7481 -0.01489 0.26660
## 4000 meter buffer -1.5360 -1.28900 0.31630
## 2750 meter buffer -0.6417 -0.88420 0.65290
## 3750 meter buffer -1.2830 -1.28300 0.43740
## 500 meter buffer 0.2211 0.36930 0.26320
## 3000 meter buffer -0.8074 -0.95970 0.57610
## 250 meter buffer -1.0680 0.14490 -0.06256
## 3500 meter buffer -1.1860 -0.91950 0.48310
## 3250 meter buffer -1.2270 -0.76390 0.52800
## cnd(scl(lc_mxd)) cnd(scl(lc_shr)) cnd(scl(osm_ind))
## 4750 meter buffer -2.1350 -0.2193 0.06849
## 5000 meter buffer -2.0760 -0.1973 0.11500
## 4500 meter buffer -2.0420 -0.3040 0.05564
## 1000 meter buffer -0.4364 -0.5876 -0.38600
## 1250 meter buffer -0.5442 -0.6927 -0.37030
## 1500 meter buffer -0.5289 -0.6217 -0.22830
## 1750 meter buffer -0.5698 -0.5523 -0.15290
## 2500 meter buffer -0.8063 -0.6174 -0.54210
## 2250 meter buffer -0.5755 -0.6210 -0.50300
## 4250 meter buffer -1.8380 -0.4017 -0.30790
## 2000 meter buffer -0.5087 -0.5390 -0.16830
## 750 meter buffer -0.2679 -0.6480 -0.14420
## 4000 meter buffer -1.5690 -0.4128 -1.27300
## 2750 meter buffer -0.8625 -0.4557 -0.33110
## 3750 meter buffer -1.3670 -0.4864 -1.30000
## 500 meter buffer 0.2701 -0.2161 0.33850
## 3000 meter buffer -1.0320 -0.5721 -0.46830
## 250 meter buffer -0.3924 -1.0100 0.08637
## 3500 meter buffer -1.1970 -0.5730 -0.55310
## 3250 meter buffer -1.1790 -0.7950 -0.26230
## cnd(scl(ppl)) cnd(scl(rds)) cnd(scl(ssm_lns_3D))
## 4750 meter buffer 0.55450 1.7720 -0.25980
## 5000 meter buffer 0.57000 1.5980 -0.33860
## 4500 meter buffer 0.35170 1.4700 -0.30540
## 1000 meter buffer -0.08652 0.9211 -0.43730
## 1250 meter buffer 0.13300 0.8973 -0.45770
## 1500 meter buffer -0.05386 0.6397 -0.36090
## 1750 meter buffer -0.23330 0.9070 -0.32960
## 2500 meter buffer -0.66470 1.5980 -0.13210
## 2250 meter buffer -0.58010 1.2340 -0.26340
## 4250 meter buffer 0.26040 1.2310 -0.49290
## 2000 meter buffer -0.47410 0.9593 -0.31730
## 750 meter buffer 0.28020 0.5181 -0.25280
## 4000 meter buffer 0.12790 1.8010 -0.41760
## 2750 meter buffer -0.43360 1.4350 -0.10180
## 3750 meter buffer 0.14840 1.7190 -0.33560
## 500 meter buffer 0.04199 0.1326 -0.17750
## 3000 meter buffer -0.25270 1.5540 -0.06029
## 250 meter buffer -0.12160 0.2010 0.02755
## 3500 meter buffer 0.02500 1.1300 -0.14100
## 3250 meter buffer -0.19720 0.7720 -0.13030
## cnd(scl(ssm_lns)) cnd(scl(trl)) cnd(scl(trn_lns))
## 4750 meter buffer -0.778800 -1.1220 0.51430
## 5000 meter buffer -0.668900 -0.9373 0.37600
## 4500 meter buffer -0.689600 -1.1380 0.65820
## 1000 meter buffer -0.034770 -0.6892 -0.84680
## 1250 meter buffer -0.001617 -0.7688 -0.74120
## 1500 meter buffer 0.012890 -0.8887 -0.10820
## 1750 meter buffer 0.024470 -0.9706 0.07963
## 2500 meter buffer 0.207500 -0.8769 0.32580
## 2250 meter buffer 0.257900 -0.8396 0.10360
## 4250 meter buffer -0.498100 -0.8505 0.68850
## 2000 meter buffer 0.080410 -0.9469 0.09995
## 750 meter buffer -0.027740 -0.6650 -0.73090
## 4000 meter buffer -0.264100 -0.5710 0.59510
## 2750 meter buffer 0.029550 -0.7075 0.32390
## 3750 meter buffer -0.155200 -0.3596 0.42950
## 500 meter buffer -0.182500 -0.6500 -0.39050
## 3000 meter buffer 0.069910 -0.5419 0.33260
## 250 meter buffer -0.056660 -0.9442 -0.44440
## 3500 meter buffer -0.089060 -0.3220 0.28860
## 3250 meter buffer 0.111800 -0.3295 0.29970
## cnd(scl(veg_edg)) cnd(scl(wll)) df logLik AICc delta
## 4750 meter buffer 2.11400 -2.1910 18 -149.588 338.4 0.00
## 5000 meter buffer 2.24100 -1.7140 18 -151.928 343.1 4.68
## 4500 meter buffer 1.65400 -1.8260 18 -152.395 344.0 5.62
## 1000 meter buffer 0.32020 -0.5817 18 -155.295 349.8 11.41
## 1250 meter buffer 0.47220 -0.7001 18 -155.337 349.9 11.50
## 1500 meter buffer 0.72530 -1.0730 18 -155.370 350.0 11.56
## 1750 meter buffer 0.68240 -1.1560 18 -155.922 351.1 12.67
## 2500 meter buffer 0.71000 -1.3560 18 -156.600 352.4 14.02
## 2250 meter buffer 0.62500 -1.2220 18 -157.202 353.6 15.23
## 4250 meter buffer 1.11700 -0.8839 18 -158.164 355.5 17.15
## 2000 meter buffer 0.65430 -1.0220 18 -158.480 356.2 17.78
## 750 meter buffer 0.38590 -0.5004 18 -159.420 358.1 19.67
## 4000 meter buffer 0.77010 -0.5758 18 -161.281 361.8 23.39
## 2750 meter buffer 0.75080 -1.0640 18 -161.352 361.9 23.53
## 3750 meter buffer 0.85350 -0.6662 18 -162.517 364.2 25.86
## 500 meter buffer 0.37150 0.1016 18 -162.994 365.2 26.81
## 3000 meter buffer 0.69030 -0.9685 18 -163.140 365.5 27.10
## 250 meter buffer -0.00829 0.1530 18 -163.822 366.9 28.47
## 3500 meter buffer 0.85220 -0.7915 18 -165.724 370.7 32.27
## 3250 meter buffer 0.75900 -0.4774 18 -166.878 373.0 34.58
## weight
## 4750 meter buffer 0.855
## 5000 meter buffer 0.082
## 4500 meter buffer 0.052
## 1000 meter buffer 0.003
## 1250 meter buffer 0.003
## 1500 meter buffer 0.003
## 1750 meter buffer 0.002
## 2500 meter buffer 0.001
## 2250 meter buffer 0.000
## 4250 meter buffer 0.000
## 2000 meter buffer 0.000
## 750 meter buffer 0.000
## 4000 meter buffer 0.000
## 2750 meter buffer 0.000
## 3750 meter buffer 0.000
## 500 meter buffer 0.000
## 3000 meter buffer 0.000
## 250 meter buffer 0.000
## 3500 meter buffer 0.000
## 3250 meter buffer 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)For red fox the top model is 4750 m buffer
Model summary 4750m
Let’s get the model summary
summary(fox_mods$`4750 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(red_fox, absent_red_fox) ~ scale(harvest) + scale(pipeline) +
## scale(roads) + scale(seismic_lines) + scale(seismic_lines_3D) +
## scale(trails) + scale(transmission_lines) + scale(veg_edges) +
## scale(wells) + scale(osm_industrial) + scale(lc_grassland) +
## scale(lc_coniferous) + scale(lc_broadleaf) + scale(lc_mixed) +
## scale(lc_developed) + scale(lc_shrub) + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 335.2 397.2 -149.6 299.2 214
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 10.88 3.298
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.48167 1.42811 -3.838 0.000124 ***
## scale(harvest) 0.33898 0.36242 0.935 0.349620
## scale(pipeline) 0.55450 0.58497 0.948 0.343173
## scale(roads) 1.77163 0.62648 2.828 0.004685 **
## scale(seismic_lines) -0.77875 0.35358 -2.203 0.027629 *
## scale(seismic_lines_3D) -0.25981 0.62085 -0.418 0.675594
## scale(trails) -1.12241 0.52768 -2.127 0.033413 *
## scale(transmission_lines) 0.51430 0.28582 1.799 0.071957 .
## scale(veg_edges) 2.11425 0.61075 3.462 0.000537 ***
## scale(wells) -2.19119 0.74700 -2.933 0.003354 **
## scale(osm_industrial) 0.06849 0.50631 0.135 0.892402
## scale(lc_grassland) 0.45871 0.31291 1.466 0.142671
## scale(lc_coniferous) -1.73437 0.97937 -1.771 0.076576 .
## scale(lc_broadleaf) -0.59669 0.71105 -0.839 0.401381
## scale(lc_mixed) -2.13469 0.56671 -3.767 0.000165 ***
## scale(lc_developed) -1.94622 0.69304 -2.808 0.004981 **
## scale(lc_shrub) -0.21928 0.60779 -0.361 0.718262
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Looks good so far
Subset Models
Add later if deemed necessary.
Top model
White-tailed deer
Global models
deer_mods <- osm_final_df_2021_2022 %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
# have to include the `` around the white-tailed_deer or R won't recognize it as a variable because of the -
glmmTMB::glmmTMB(cbind(`white-tailed_deer`, `absent_white-tailed_deer`) ~
# HFI
scale(harvest) +
scale(pipeline) +
scale(roads) +
scale(seismic_lines) +
scale(seismic_lines_3D) +
scale(trails) +
scale(transmission_lines) +
scale(veg_edges) +
scale(wells) +
scale(osm_industrial) +
# VEG covariates in numerical order
scale(lc_grassland) +
scale(lc_coniferous) +
scale(lc_broadleaf) +
scale(lc_mixed) +
scale(lc_developed) +
scale(lc_shrub) +
# Random effect of array
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(deer_mods)## Model selection table
## cnd((Int)) dsp((Int)) cnd(scl(hrv)) cnd(scl(lc_brd))
## 1500 meter buffer -0.2043 + -0.14420 0.145500
## 1250 meter buffer -0.1948 + -0.15150 0.173400
## 1750 meter buffer -0.2032 + -0.15680 0.113000
## 4000 meter buffer -0.2110 + -0.29720 -0.072220
## 3750 meter buffer -0.2075 + -0.28380 -0.049260
## 3500 meter buffer -0.2083 + -0.26840 -0.018200
## 4250 meter buffer -0.2113 + -0.30650 -0.086260
## 2000 meter buffer -0.2051 + -0.17040 0.139300
## 4500 meter buffer -0.2117 + -0.30190 -0.139500
## 3250 meter buffer -0.2060 + -0.23600 -0.028290
## 4750 meter buffer -0.2111 + -0.29520 -0.199600
## 5000 meter buffer -0.2104 + -0.29120 -0.255600
## 2250 meter buffer -0.2013 + -0.19010 0.116300
## 3000 meter buffer -0.2015 + -0.20990 -0.001321
## 2750 meter buffer -0.2006 + -0.19840 0.061800
## 2500 meter buffer -0.2004 + -0.18860 0.125000
## 1000 meter buffer -0.1789 + -0.16970 0.237800
## 750 meter buffer -0.1827 + -0.12340 0.159000
## 500 meter buffer -0.1890 + -0.04165 -0.040670
## 250 meter buffer -0.1830 + 0.02303 -0.103900
## cnd(scl(lc_cnf)) cnd(scl(lc_dvl)) cnd(scl(lc_grs))
## 1500 meter buffer -0.17350 -0.007861 0.15040
## 1250 meter buffer -0.12080 0.038340 0.13320
## 1750 meter buffer -0.20820 -0.026170 0.14180
## 4000 meter buffer -0.52070 -0.136700 0.28260
## 3750 meter buffer -0.48300 -0.157200 0.24990
## 3500 meter buffer -0.41060 -0.117800 0.24370
## 4250 meter buffer -0.56400 -0.179700 0.29920
## 2000 meter buffer -0.14010 0.001791 0.17340
## 4500 meter buffer -0.65010 -0.212100 0.31300
## 3250 meter buffer -0.38630 -0.079840 0.23610
## 4750 meter buffer -0.73840 -0.208000 0.30060
## 5000 meter buffer -0.83550 -0.215400 0.27990
## 2250 meter buffer -0.16340 -0.025840 0.17270
## 3000 meter buffer -0.33820 -0.055770 0.22780
## 2750 meter buffer -0.23950 -0.027520 0.20890
## 2500 meter buffer -0.16090 -0.037020 0.20250
## 1000 meter buffer -0.02351 0.100100 0.10230
## 750 meter buffer -0.08746 0.155200 0.06230
## 500 meter buffer -0.37420 0.078840 -0.01527
## 250 meter buffer -0.56080 -0.001368 -0.08770
## cnd(scl(lc_mxd)) cnd(scl(lc_shr)) cnd(scl(osm_ind))
## 1500 meter buffer 0.28160 0.033630 0.4141
## 1250 meter buffer 0.28580 0.054670 0.4178
## 1750 meter buffer 0.26060 0.006141 0.3574
## 4000 meter buffer 0.21600 -0.118900 0.1799
## 3750 meter buffer 0.23940 -0.095690 0.1647
## 3500 meter buffer 0.26370 -0.061460 0.1660
## 4250 meter buffer 0.16210 -0.139000 0.1654
## 2000 meter buffer 0.30840 0.053940 0.3139
## 4500 meter buffer 0.09763 -0.193100 0.1473
## 3250 meter buffer 0.23790 -0.056530 0.1791
## 4750 meter buffer 0.02768 -0.264300 0.1247
## 5000 meter buffer -0.04817 -0.329700 0.1121
## 2250 meter buffer 0.28140 0.037560 0.2715
## 3000 meter buffer 0.24280 -0.036920 0.2004
## 2750 meter buffer 0.26830 0.010970 0.2197
## 2500 meter buffer 0.27030 0.047620 0.2355
## 1000 meter buffer 0.29890 0.066870 0.3539
## 750 meter buffer 0.16780 0.014890 0.3334
## 500 meter buffer -0.04650 -0.137800 0.2630
## 250 meter buffer -0.16920 -0.236400 0.1880
## cnd(scl(ppl)) cnd(scl(rds)) cnd(scl(ssm_lns_3D))
## 1500 meter buffer 0.058750 -0.1019000 -0.4396
## 1250 meter buffer 0.095410 -0.0485900 -0.4204
## 1750 meter buffer 0.014140 -0.0007351 -0.3973
## 4000 meter buffer 0.002368 -0.1157000 -0.2863
## 3750 meter buffer 0.101400 -0.0711400 -0.3089
## 3500 meter buffer 0.132100 -0.0364700 -0.3089
## 4250 meter buffer -0.020040 -0.0965900 -0.2656
## 2000 meter buffer -0.005024 0.0196300 -0.3705
## 4500 meter buffer -0.089730 -0.0727300 -0.2495
## 3250 meter buffer 0.126100 -0.0133700 -0.2967
## 4750 meter buffer -0.195000 -0.0433200 -0.2262
## 5000 meter buffer -0.279500 -0.0379400 -0.2021
## 2250 meter buffer -0.007749 0.0586200 -0.3343
## 3000 meter buffer 0.057480 0.0145900 -0.2858
## 2750 meter buffer 0.020450 0.0491900 -0.2936
## 2500 meter buffer 0.018310 0.0742400 -0.3052
## 1000 meter buffer 0.129300 0.0047080 -0.3375
## 750 meter buffer 0.114200 -0.0349300 -0.2754
## 500 meter buffer 0.096410 -0.0344700 -0.2306
## 250 meter buffer 0.105900 -0.0198700 -0.2150
## cnd(scl(ssm_lns)) cnd(scl(trl)) cnd(scl(trn_lns))
## 1500 meter buffer -0.14640 0.11130 -0.053540
## 1250 meter buffer -0.13820 0.10560 -0.104600
## 1750 meter buffer -0.14340 0.11520 -0.003427
## 4000 meter buffer -0.08145 0.20080 -0.126600
## 3750 meter buffer -0.07416 0.20910 -0.181200
## 3500 meter buffer -0.06994 0.19850 -0.225800
## 4250 meter buffer -0.10260 0.19180 -0.135000
## 2000 meter buffer -0.13400 0.11600 -0.012350
## 4500 meter buffer -0.09499 0.18090 -0.102900
## 3250 meter buffer -0.04894 0.18850 -0.205200
## 4750 meter buffer -0.07050 0.16710 -0.061650
## 5000 meter buffer -0.04943 0.15980 -0.044950
## 2250 meter buffer -0.09048 0.13470 -0.041230
## 3000 meter buffer -0.03858 0.16090 -0.128600
## 2750 meter buffer -0.05687 0.15650 -0.081490
## 2500 meter buffer -0.07414 0.15470 -0.080920
## 1000 meter buffer -0.12240 0.12710 -0.124100
## 750 meter buffer -0.04965 0.06837 0.008704
## 500 meter buffer 0.01582 0.08367 0.071480
## 250 meter buffer -0.05631 0.16840 -0.011320
## cnd(scl(veg_edg)) cnd(scl(wll)) df logLik AICc delta
## 1500 meter buffer -0.242100 0.39620 18 -537.053 1113.3 0.00
## 1250 meter buffer -0.249000 0.32870 18 -541.160 1121.5 8.22
## 1750 meter buffer -0.278300 0.37420 18 -541.829 1122.9 9.55
## 4000 meter buffer 0.001398 0.40600 18 -544.404 1128.0 14.70
## 3750 meter buffer 0.016500 0.36800 18 -544.421 1128.1 14.74
## 3500 meter buffer -0.021160 0.34410 18 -545.857 1130.9 17.61
## 4250 meter buffer 0.060980 0.38260 18 -545.869 1130.9 17.63
## 2000 meter buffer -0.274900 0.37520 18 -546.342 1131.9 18.58
## 4500 meter buffer 0.104900 0.38530 18 -546.625 1132.5 19.14
## 3250 meter buffer -0.085550 0.35550 18 -548.339 1135.9 22.57
## 4750 meter buffer 0.124800 0.39700 18 -548.938 1137.1 23.77
## 5000 meter buffer 0.138100 0.43710 18 -549.440 1138.1 24.77
## 2250 meter buffer -0.232100 0.39110 18 -550.150 1139.5 26.19
## 3000 meter buffer -0.133500 0.36960 18 -551.018 1141.2 27.93
## 2750 meter buffer -0.194200 0.38230 18 -551.979 1143.2 29.85
## 2500 meter buffer -0.183800 0.35610 18 -552.560 1144.3 31.01
## 1000 meter buffer -0.199000 0.27410 18 -552.713 1144.6 31.32
## 750 meter buffer -0.239500 0.25260 18 -568.054 1175.3 62.00
## 500 meter buffer -0.221400 0.21880 18 -573.059 1185.3 72.01
## 250 meter buffer -0.215200 0.05339 18 -574.675 1188.6 75.24
## weight
## 1500 meter buffer 0.974
## 1250 meter buffer 0.016
## 1750 meter buffer 0.008
## 4000 meter buffer 0.001
## 3750 meter buffer 0.001
## 3500 meter buffer 0.000
## 4250 meter buffer 0.000
## 2000 meter buffer 0.000
## 4500 meter buffer 0.000
## 3250 meter buffer 0.000
## 4750 meter buffer 0.000
## 5000 meter buffer 0.000
## 2250 meter buffer 0.000
## 3000 meter buffer 0.000
## 2750 meter buffer 0.000
## 2500 meter buffer 0.000
## 1000 meter buffer 0.000
## 750 meter buffer 0.000
## 500 meter buffer 0.000
## 250 meter buffer 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)For deer the top model was also the 1500 buffer
Model summary 1500m
Let’s get the model summary
summary(deer_mods$`1500 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(`white-tailed_deer`, `absent_white-tailed_deer`) ~ scale(harvest) +
## scale(pipeline) + scale(roads) + scale(seismic_lines) + scale(seismic_lines_3D) +
## scale(trails) + scale(transmission_lines) + scale(veg_edges) +
## scale(wells) + scale(osm_industrial) + scale(lc_grassland) +
## scale(lc_coniferous) + scale(lc_broadleaf) + scale(lc_mixed) +
## scale(lc_developed) + scale(lc_shrub) + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 1110.1 1172.1 -537.1 1074.1 214
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 1.107 1.052
## Number of obs: 232, groups: array, 6
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.204311 0.432747 -0.472 0.6368
## scale(harvest) -0.144219 0.068902 -2.093 0.0363 *
## scale(pipeline) 0.058746 0.107685 0.546 0.5854
## scale(roads) -0.101872 0.146611 -0.695 0.4872
## scale(seismic_lines) -0.146367 0.069900 -2.094 0.0363 *
## scale(seismic_lines_3D) -0.439551 0.096937 -4.534 5.78e-06 ***
## scale(trails) 0.111275 0.053479 2.081 0.0375 *
## scale(transmission_lines) -0.053540 0.118825 -0.451 0.6523
## scale(veg_edges) -0.242143 0.130037 -1.862 0.0626 .
## scale(wells) 0.396152 0.088212 4.491 7.09e-06 ***
## scale(osm_industrial) 0.414131 0.068684 6.030 1.64e-09 ***
## scale(lc_grassland) 0.150428 0.076396 1.969 0.0489 *
## scale(lc_coniferous) -0.173514 0.238731 -0.727 0.4673
## scale(lc_broadleaf) 0.145490 0.169488 0.858 0.3907
## scale(lc_mixed) 0.281594 0.155049 1.816 0.0693 .
## scale(lc_developed) -0.007861 0.118023 -0.067 0.9469
## scale(lc_shrub) 0.033628 0.139233 0.242 0.8091
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Looks okay for now
Subset models
Add later if deemed necessary
Top model
Top buffers
For convenience here is a list of the top buffer widths for each species
- Black bear - 250m
- Caribou - 3000m
- Coyote - 2250m
- Fisher - 1000m
- Grey wolf - 500m
- Lynx - 1250m
- Moose - 250m
- Red fox - 4750
- White-tailed deer - 1500m
Plots for all species
Buffer size
What I want to do here is create a figure that plots a point for each species at the buffer size that was in the top model.
Data frame
Before we can do this I need to make a data frame that include the species, top buffer width, and any other info I may want in the plot.
buffer_data <- tibble(
#define number of rows
.rows = 9,
# create species column
species = c('Black bear',
'Caribou',
'Coyote',
'Fisher',
'Grey wolf',
'Lynx',
'Moose',
'Red fox',
'White-tailed deer'),
# create buffer column w/ buffer corresponding to the top model for each species
buffer_width = c(250,
3000,
2250,
1000,
500,
1250,
250,
4750,
1500),
# add phylopic uuid for each species for plotting
image = c(get_uuid(name = 'Ursus americanus'),
get_uuid(name = 'Rangifer tarandus'),
get_uuid(name = 'Canis latrans'),
get_uuid(name = 'Pekania pennanti'),
get_uuid(name = 'Canis lupus'),
get_uuid(name = 'Lynx lynx'),
get_uuid(name = 'Alces alces'),
get_uuid(name = 'Vulpes vulpes'),
get_uuid(name ='Odocoileus virginianus'))
)Plot
ggplot(buffer_data,
aes( x = species,
y = buffer_width)) +
# add points as phylopic silhouettes
geom_phylopic(aes(uuid = image),
size = 500) +
scale_y_continuous(breaks = seq(0, 5000, by = 250)) +
coord_cartesian(ylim = c(0, 4900))
This looks alright but I don’t love all the default phylopic images that
are used.
Fix phylopics
We can go to the Phylopic website and check out other options and then use the code below to search for the uuid from those options for the ones we want to replace
# Caribou could be bigger
get_uuid(name = 'Rangifer tarandus',
n = 6) ## [1] "09c5d6bf-8ed9-4dc3-86f1-6b75dbe63952"
## [2] "a7e8f29e-a4e2-499a-b913-491f78f44f1d"
## [3] "ad1de77a-c89b-41ad-801c-1ef47c960c83"
## [4] "d2e268c1-1937-4308-9fa4-9adc55cc5e8b"
## [5] "e6e864fd-8e3d-435f-9db3-dc6869c589f1"
## [6] "19864ae2-b740-4a41-9f91-e3a195ee7cbb"# I like the 5th one best
# Coyote doesn't need to have a food item
get_uuid(name = 'Canis latrans',
n = 6) ## [1] "113d2520-9f92-456f-b305-52ee3986172d"
## [2] "3492f4ca-01f0-4609-a84f-084a84bf4e95"
## [3] "5a0398e3-a455-4ca6-ba86-cf3f1b25977a"
## [4] "76c8fdec-d0af-47b9-b949-dc610419d832"
## [5] "da5faa63-085f-4523-a542-e71cb386c999"
## [6] "e6a2fa4b-85df-43b4-989c-34a65ba7eee3"# I like the 6th one best
# Grey wolf shouldn't be the same as coyote
get_uuid(name = 'Canis lupus',
n = 5) ## [1] "113d2520-9f92-456f-b305-52ee3986172d"
## [2] "11658f8c-e0c2-4612-85ef-bdc44acdae0b"
## [3] "3492f4ca-01f0-4609-a84f-084a84bf4e95"
## [4] "5a0398e3-a455-4ca6-ba86-cf3f1b25977a"
## [5] "76c8fdec-d0af-47b9-b949-dc610419d832"# I like 2 or 5
# Moose needs a body
get_uuid(name = 'Alces alces',
n = 3) ## [1] "df2d0ad0-adb0-49d7-afe5-edc6cad21064"
## [2] "1a20a65d-1342-4833-a9dd-1611b9fb383c"
## [3] "74eab34a-498c-4614-aece-f02361874f79"# 3 is best
# red fox is too big
get_uuid(name = 'Vulpes vulpes',
n = 7) ## [1] "76352962-1eeb-4197-acdd-e3c7eeab839d"
## [2] "9b3e3567-2238-40bf-ab5d-bcb6ce7b32d5"
## [3] "a5e2a085-c895-4fdd-b39e-bbac8ca94d7d"
## [4] "d67d3bf6-3509-4ab6-819a-cd409985347e"
## [5] "40541f06-01d8-4585-a271-40268d24057d"
## [6] "40a5954e-ede1-4af6-9f65-209d1780e602"
## [7] "51b1b6e4-129d-41a6-bbbd-c3fab459c25f"# 4 is best
# deer is weird
get_uuid(name = 'Odocoileus virginianus',
n = 6) ## [1] "4584be20-4514-4673-a3e8-97e2a6a10e57"
## [2] "49a5a5db-047e-4e17-849b-9f96a93f0d2b"
## [3] "56f6fdb2-15d0-43b5-b13f-714f2cb0f5d0"
## [4] "9df2ed00-b918-4d3a-82bd-1a94283f7c0f"
## [5] "6038e80c-398d-47b2-9a69-2b9edf436f64"
## [6] "8569838c-c725-4772-b0a3-b5eb04baaada"# 5 is bestLet’s alter the code from above to change the phylopic images
buffer_data <- tibble(
#define number of rows
.rows = 9,
# create species column
species = c('Black bear',
'Caribou',
'Coyote',
'Fisher',
'Grey wolf',
'Lynx',
'Moose',
'Red fox',
'White-tailed deer'),
# create buffer column w/ buffer corresponding to the top model for each species
buffer_width = c(250,
3000,
2250,
1000,
500,
1250,
250,
4750,
1500),
# add phylopic uuid for each species for plotting
image = c(get_uuid(name = 'Ursus americanus'),
'e6e864fd-8e3d-435f-9db3-dc6869c589f1', # caribou 5
'e6a2fa4b-85df-43b4-989c-34a65ba7eee3', # coyote 6
get_uuid(name = 'Pekania pennanti'),
'76c8fdec-d0af-47b9-b949-dc610419d832', # wolf 5
get_uuid(name = 'Lynx lynx'),
'74eab34a-498c-4614-aece-f02361874f79', # moose 3
'd67d3bf6-3509-4ab6-819a-cd409985347e', # red fox 4
'6038e80c-398d-47b2-9a69-2b9edf436f64') # white-tailed deer 6
)Plot again
Using the code from above with the fixed images let’s plot it again
buffer_plot <- ggplot(buffer_data,
aes( x = species,
y = buffer_width)) +
# add points as phylopic silhouettes
geom_phylopic(aes(uuid = image),
size = 400) +
# set axis limits and breaks
scale_y_continuous(breaks = seq(0, 5000, by = 500)) +
# wrap long species names
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
# set graph limits
coord_cartesian(ylim = c(0,4900)) +
# make nicer labels
labs(y = 'Buffer width (meters)') +
# set theme
theme_bw() +
theme(panel.grid.minor = element_blank(),
axis.text = element_text(size = 14),
axis.title = element_text(size = 16))
buffer_plot 
BEAUTIFUL!!! If I do say so myself
Save plot
ggsave('figures/OSM_species_buffers_2022.jpeg',
buffer_plot,
width = 18,
height = 12,
units = 'in',
dpi = 600)Body size plot
Now I want to see if their are trends between body size and which bfuffer distance performed best. I’ll first need to add a body size column to the data.
Mutate data
To do this I’m going to google the avergae body size of each species and then add it as a column using mutate
** IMPORTANT - this is just for data visualization purposes!. Since we don’t actually have data on body size of the individuals in our study I can’t run any analysis on this but I can use it to visualize if there appears to be any trends worth investigating.
# first add body size to buffer data
body_size_data <- buffer_data %>%
# use mutate to create new column
mutate(body_size = case_when(species == 'Black bear' ~ 181,
species == 'Caribou' ~ 355,
species == 'Coyote' ~ 15,
species == 'Fisher' ~ 3.5,
species == 'Grey wolf' ~ 39,
species == 'Lynx' ~ 11,
species == 'Moose' ~ 555,
species == 'Red fox' ~ 5.2,
species == 'White-tailed deer' ~ 200),
log_body_size = log(body_size))Plot
Now let’s plot this as a scatterplot
body_size_plot <- ggplot(body_size_data, aes(x = log_body_size,
y = buffer_width)) +
# add points as phylopic silhouettes
geom_phylopic(aes(uuid = image),
size = 400) +
labs(x = 'Log body size (kg)',
y = 'Buffer width (meters)') +
# set theme
theme_bw() +
theme(panel.grid.minor = element_blank(),
axis.text = element_text(size = 14),
axis.title = element_text(size = 16))
body_size_plot
Save plot
ggsave('figures/OSM_body_size_buffers_2022.jpg',
body_size_plot,
width = 18,
height = 12,
units = 'in',
dpi = 600)Odds ratio plots
What we need to do now is extract the coefficient estimates from each top model, as well as the 95% CI so we can plot them for easier visualization and understanding of the data
Extract odds ratios
The confint() function will extract the coefficients and
CI intervals from a model, so what we need to do is make a list of all
the models, then use the map() function in purrr
to apply the confint() function to all the models and get
the coefficients. We want this to result in a tibble that has a column
for the HFI feature (we aren’t plotting the lc_class data for this
report), the upper and lower CI, and the coefficient estimate.
In order to do this we have to do a bit of data wrangling, currently this isn’t the most pleasing way to accomplish the desired outcome, but it works.
# This is also a dog shit way to do this but I need to get this done
# make a list of the top models for each species
top_models <- list(black_bear_mods$`250 meter buffer`,
caribou_mods$`3000 meter buffer`,
coyote_mods$`2250 meter buffer`,
fisher_mods$`1000 meter buffer`,
wolf_mods$`500 meter buffer`,
lynx_mods$`1250 meter buffer`,
moose_mods$`250 meter buffer`,
fox_mods$`4750 meter buffer`,
deer_mods$`1500 meter buffer`) %>%
# pipe into purrr to create coefficient table for all models
purrr::map(
~.x %>%
# extract the coefficients and upper and lower CI
confint() %>%
# format resulting object as a tibble data frame
as_tibble() %>%
# subset to just the HFI variables for these plots
slice_head(n = 11) %>%
# remove first row which is the intercept
slice_tail(n = 10) %>%
# add a column where we can put the feature names
rowid_to_column() %>%
# rename the columns for plotting
rename('lower' = `2.5 %`,
'upper' = `97.5 %`,
'estimate' = Estimate,
'feature' = rowid) %>%
# rename the entries to features, need to look at the order the features are in from the model summary and ensure it matches
mutate(feature = as.factor(feature),
feature = recode(feature,
'1' = 'harvest',
'2' = 'pipeline',
'3' = 'roads',
'4' = 'seismic_lines',
'5' = 'seismic_lines_3D',
'6' = 'trails',
'7' = 'transmission_lines',
'8' = 'veg_edges',
'9' = 'wells',
'10' = 'osm_industrial'))) %>%
# set the names of each resulting tibble data frame to the species name
purrr::set_names('Black bear',
'Caribou',
'Coyote',
'Fisher',
'Grey wolf',
'Lynx',
'Moose',
'Red fox',
'White-tailed deer')Merge data frames
Now we have a data frame with the odds ratios for each species, but if we want these on a plot together we need them all in one data frame.
To merge data into one data frame we can use the
list_rbind() function from the purrr package which
will take each element of the list and stack them on top of one another
just like rbind does with data frames, and if we use the names_to
argument we can extract the names of the list elements and assign them
to a column so we know which data comes from which species model (list
element)
In this code I also add a new column called uuid which contains the image id (uuid) for a phylopic silhouette of each species that I may want to use for plotting
Phylopic.or is an open source
online database of silhouettes various contributors have created for
use. There is an R package that works with this data called
rphylopic; we can use the get_uuid() function from
this package to extract the data for a silhouette for each species we
want, which is what I’ve done here.
# combine all list elements
coeffs_df_all <- list_rbind(top_models,
names_to = 'species') %>%
# change species to a factor for plotting
mutate(species = as.factor(species),
# add phylopic uuid for each species for plotting
# the uuid is extracted using getuuid with the species name as name = ''
uuid = case_when(species == 'Black bear' ~ get_uuid(name = 'Ursus americanus'),
species == 'Caribou' ~ get_uuid(name = 'Rangifer tarandus'),
species == 'Coyote' ~ get_uuid(name = 'Canis latrans'),
species == 'Fisher' ~ get_uuid(name = 'Pekania pennanti'),
species == 'Grey wolf' ~ get_uuid(name = 'Canis lupus'),
species == 'Lynx' ~ get_uuid(name = 'Lynx lynx'),
species == 'Moose' ~ get_uuid(name = 'Alces alces'),
species == 'Red fox' ~ get_uuid(name = 'Vulpes vulpes'),
species == 'White-taield deer' ~ get_uuid(name ='Odocoileus virginianus'))) Plot odds all species
Now let’s explore some different options to plot the coefficients
geom_phylopic
Let’s try plotting all the species on one plot using
ggplot()
# provide data and mapping aesthetics
ggplot(coeffs_df_all, aes(x = feature,
y = estimate,
group = uuid)) +
geom_errorbar(aes(ymin = lower,
ymax = upper,
color = feature),
width = 0.4,
linewidth = 0.5,
position = position_dodge(width = 0.9)) +
# add points for each estimate for each covariate and use position = position_dodge to shift the points so all the species don't plot on top of one another
geom_phylopic(aes(x = feature,
y = estimate,
uuid = uuid),
position = position_dodge(width = 0.9),
size = 0.3)## Warning: Removed 10 rows containing missing values.
This works but unfortunately is very messy with the images as the points since they don’t all have the same centerline and are different sizes.
I try with the code below to select better silhouettes
# combine all list elements
coeffs_df_all <- list_rbind(top_models,
names_to = 'species') %>%
# change species to a factor for plotting
mutate(species = as.factor(species),
# add phylopic uuid for each species for plotting
# the uuid is extracted using getuuid with the species name as name = ''
uuid = case_when(species == 'Black bear' ~ get_uuid(name = 'Ursus americanus'),
species == 'Caribou' ~ 'e6e864fd-8e3d-435f-9db3-dc6869c589f1', # caribou 5
species == 'Coyote' ~ 'e6a2fa4b-85df-43b4-989c-34a65ba7eee3', # coyote 6
species == 'Fisher' ~ '735066c6-2f3e-4f97-acb1-06f55ae075c9',
species == 'Grey wolf' ~ '76c8fdec-d0af-47b9-b949-dc610419d832', # wolf 5
species == 'Lynx' ~ '74eab34a-498c-4614-aece-f02361874f79', # moose 3
species == 'Moose' ~ '74eab34a-498c-4614-aece-f02361874f79',
species == 'Red fox' ~ 'd67d3bf6-3509-4ab6-819a-cd409985347e', # red fox 4
species == 'White-taield deer' ~ '6038e80c-398d-47b2-9a69-2b9edf436f64')) # white-tailed deer 6Try plotting all again
ggplot(coeffs_df_all, aes(x = feature,
y = estimate,
group = uuid)) +
geom_errorbar(aes(ymin = lower,
ymax = upper,
color = feature),
width = 0.4,
linewidth = 0.5,
position = position_dodge(width = 1.2)) +
# add points for each estimate for each covariate and use position = position_dodge to shift the points so all the species don't plot on top of one another
geom_phylopic(aes(x = feature,
y = estimate,
uuid = uuid),
position = position_dodge(width = 1.2),
size = 0.2)## Warning: Removed 10 rows containing missing values.## Warning: `position_dodge()` requires non-overlapping x intervals
Unfortunately they still don’t center on the lines like they should so will try a different plot
Facet_wrap
ggplot(coeffs_df_all,
aes(x = feature,
y = estimate)) +
# add points
geom_point() +
# add errorbar
geom_errorbar(aes(ymin = lower,
ymax = upper),
width = 0.4,
linewidth = 0.5) +
facet_wrap(vars(species)) +
# wrap long axis names
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
# adjust theme elements
theme(axis.text.x = element_text(angle = 90,
hjust = 1),
axis.ticks.x = element_blank())
Plot_model
If all else fails can use plot_model function
Example with black bear model
library(sjPlot)## Install package "strengejacke" from GitHub (`devtools::install_github("strengejacke/strengejacke")`) to load all sj-packages at once!plot_model(black_bear_mods$`250 meter buffer`,
vline.color = 'black')